PREDICTION OF DIABETES USING LOGISTIC REGRESSION

DEPARTMENT OF INFORMATION SCIENCE AND ENGINEERING

A PROJECT REPORT ON

“PREDICTION OF DIABETES USING LOGISTIC REGRESSION”

Submitted in the partial fulfillment of the requirements in the 8th semester of

BACHELOR OF ENGINEERING

IN

INFORMATION SCIENCE AND ENGINEERING

By

MADUKARA 1NH15IS057

Under the guidance of

Mrs. K. K. KAVITHA Assistant Professor

Dept. of ISE, NHCE


CERTIFICATE

Certified that the project work entitled “Prediction of Diabetes using logistic

Regression”, carried out by Mr.Madukara-1NH15IS057 bonafide students of NEW HORIZON

COLLEGE OF ENGINEERING, Bengaluru,in partial fulfillment for the award of Bachelor of

Engineering in Information Science and Engineering of the Visveswaraya Technological

University, Belgaum during the year 2018-19.It is certified that all corrections/suggestions

indicated for Internal Assessment have been incorporated in the Report deposited in the

departmental library.

The project report has been approved as it satisfies the academic requirements in respect of

Project work prescribed for the said Degree.

-------------------------------- --------------------------------- -----------------------------------

Prof. K. K. Kavitha Dr. R J Anandhi Dr. Manjunatha

Internal Guide HOD, Dept. of ISE Principal, NHCE

External Viva

Name of the Examiners Signature with Date

1. ______________________

2.


DECLARATION

I hereby declare that I have followed the guidelines provided by the Institution in

preparing the project report and presented report of project titled “Diabetes

Prediction”, andis uniquely prepared by me after the completion of the project

work. I also confirm that the report is only prepared for my academic requirement

and the results embodied in this report have not been submitted to any other

University or Institution for the award of any degree.

Signature of the Student Name: Madukara

USN: 1NH15IS057

i

ABSTRACT

Diabetes is a disease caused due to the increase level of blood glucose. Diabetes is a chronic

disease with the potential to cause a worldwide health care crisis. According to International

Diabetes Federation 382 million people are living with diabetes across the whole world. By

2035, this will be doubled as 592 million. Various traditional methods, based on physical and

chemical tests, are available for diagnosing diabetes. However, early prediction of diabetes is

quite challenging task for medical practitioners due to complex interdependence on various

factors as diabetes affects human organs such as kidney, eye, heart, nerves, foot etc. Data

science methods have the potential to benefit other scientific fields by shedding new light on

common questions. One such task is to help make predictions on medical data. Machine

learning is an emerging scientific field in data science dealing with the ways in which machines

learn from experience. The aim of this project is to develop a system which can perform early

prediction of diabetes for a patient with a higher accuracy by combining the results of machine

learning technique. This project aims to predict diabetes via supervised machine learning

methods Logistic regression. This project also aims to propose an effective technique for earlier

detection of the diabetes disease.

ii

ACKNOWLEDGEMENT

Any achievement, be it scholastic or otherwise does not depend solely on the individual efforts

but on the guidance, encouragement and cooperation of intellectuals, elders and friends. A

number of personalities, in their own capacities have helped me in carrying out this project. I

would like to take an opportunity to thank them all.

First and foremost I thank the management, Dr. Mohan Manghnani, Chairman, New Horizon

Educational Institutions for providing us the opportunity to do internship.

I would like to thank Dr.Manjunatha, Principal, New Horizon College of Engineering,

Bengaluru, for his constant encouragement and support extended to us towards completing my

internship.

I extend my sincere gratitude to Dr. R J Anandhi, Head of the Department, Information Science

and Engineering, New Horizon College of Engineering, Bengaluru for her valuable suggestions

and expert advice.

I deeply express my sincere gratitude to my guide Mrs. K. K. Kavitha, Assistant Professor,

Department of ISE, NHCE, Bengaluru, for his able guidance, regular source of encouragement

and assistance throughout my internship period.

Last, but not the least, I would like to thank my peers and friends who provided me with valuable

suggestions to improve my project.

Madukara

(1NH15IS057)

iii

TABLE OF CONTENTS

Abstract i

Acknowledgement ii

Table of Contents iii

List of Figures v

1. Preamble 1

1.1 Introduction 1

1.2 Relevance of the Project 3

1.3 Purpose 3

1.4 Scope of the Project 4

1.5 Problem Statement and Definition 4

1.6 Objective of study 5

1.7 Existing System 6

1.8 Proposed system 6

2. Literature Survey 8

3. System Requirements and specifications 13

3.1 General Description of the system 13

3.1.1 Overview of Functional requirements 13

3.1.2 Overview of Data requirements 15

3.2 Technical Requirements of the System 17

3.2.1 Hardware Requirements 17

3.2.2 Software Requirements 17

iv

3.3 Language specification 17

3.3.1 Python 17

3.3.2 Machine Learning Features 18 3. 3. 3 Logistic Regression 19

4. System Design and Analysis 21

4.1 Preliminary Design 21

4.2 System Architecture 22

4.3 Data Flow Diagram 23

4.3.1 DFD for Data Extraction 24

4.3.2 DFD for Classification of Data 24

4.3 Use Case Diagram 25

5. Implementation 26

5.1 Different modules of the project 31

5.2 Flow Chart Of The Proposed System 32

6. Experimental Results 33

6.1 Outcomes of the Proposed System 33

7. Testing 34

7.1 Testing and Validations 34

7.2 Testing Levels 31

7.2.1 Functional Testing 35

7.2.2 Non-Functional Testing 35

7.3 White Box Testing 37

7.4 Different Stages of Testing 38

7.4.1 Unit Testing 38

7.4.2 Integration Testing 40

7.4.3 System Testing 41

7.4.4 Acceptance Testing 41

8. Conclusion and Future Enhancement 42

8.1 Conclusion 42

v

LIST OF FIGURES

Figure no Figure Name Page no

Figure 1.1 Predicting Diabetes Using Machine Learning

1

Figure 1.2 Representation of Diabetes Level Checking

2

Figure 3.1 Training and test set 14

Figure 4.1 Preliminary design of Diabetes Prediction system

21

Figure 4.2 Architecture of Diabetes Prediction System

22

Figure 4.3 Data Flow Diagram 23

Figure 4.4 DFD for Data Extraction 24

Figure 4.5 DFD for Classification of Data 24

Figure 4.6 Use Case Diagram 25

Figure 5.1 Confusion matrix 27

Figure 5.2 ndarray 29

Figure 5.3 Dataset 31

Figure 5.2 Flowchart of the proposed system

32

Figure 6.1 Data distribution 33

Figure 7.1 Non-Functional Testing 36

vi

Diabetes Prediction

Dept of ISE, NHCE Page 1

Chapter 1

Preamble

Diabetes is one of the deadliest diseases in the world. It is not only a disease but also

a creator of kinds of diseases like heart attack , blindness, kidney diseases etc. The

normal identifying process is that patients needs to visit a diagnostic center, consult

their doctor, and sit for a day or more to get their reports. But with the rise of

Machine Learning approaches we have the ability to find a solution to this issue.

1.1 Introduction

Diabetes mellitus is an endless infection portrayed by hyperglycemia. It might cause

numerous inconveniences. As per the developing bleakness as of late, in 2040, the

world's diabetic patients will achieve 642 million, which implies that one of the ten

grown-ups later on is experiencing diabetes. There is no uncertainty this disturbing

figure needs extraordinary consideration. World Health Organization has assessed 12

million passings happen around the world, consistently because of Heart maladies. A

large portion of the passings in the United States and other created nations are

expected to cardio vascular maladies. The early visualization of cardiovascular

sicknesses can help in settling on choices on way of life changes in high hazard

patients and thus decrease the intricacies. This exploration means to pinpoint the

most significant/hazard elements of coronary illness just as anticipate the general

hazard utilizing calculated relapse. Machine Learning has been connected to

numerous parts of medicinal wellbeing. In this project, we utilized Logistic regression

to anticipate diabetes mellitus.

Figure 1.1 : Predicting Diabetes Using Machine Learning

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The dataset is originally from the National Institute of Diabetes and Digestive and

Kidney Diseases. The objective of the dataset is to diagnostically predict whether or

not a patient has diabetes, based on certain diagnostic measurements included in

the dataset.

Several constraints were placed on the selection of these instances from a larger

database. In particular, all patients here are females at least 21 years old of Pima

Indian heritage.

The datasets consist of several medical predictor (independent) variables and one

target (dependent) variable, Outcome. Independent variables include the number of

pregnancies the patient has had, their BMI, insulin level, age, and so on.

Proposed model is to anticipate diabetes that specialists can be valuable as a model

to help foresee diabetes. In this examination, analyzed the connection between

difficulties in diabetic patients and their properties, for example, blood glucose,

circulatory strain, tallness, weight, and hemoglobin and weight record of the

patients. The point of this examination is to foresee confusions dependent on their

manifestations.

Figure 1.2 : Representation of Diabetes Level Checking

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The above figure 1.2 shows how the diabetes level will be checked and gives the

alert upon increase in the level of diabetes.

1.2 Relevance of the Project

In recent years, using Machine Learning has been used with increasing frequency to

predict the possibility of disease. Many algorithms and toolkits have been created

and studied by researchers. These have highlighted the tremendous potential of

this research field.

Based on several studies, we found that a commonly used dataset was the Pima

Indians Diabetes Dataset from the University of California, Irvine (UCI) Machine

Learning Database. In this project logistic regression aimed at validating a chosen

class label of given data and aimed at building the final classifier model. All the

studies presented above used the same Pima Indians Diabetes Dataset as the

experimental material.

1.3 Purpose

Large amount of data has been continuously generated in field of engineering and

science. Recent advances in technology have resulted in big electronic data that

allow data to be captured, processed, analyzed and stored rather inexpensively.

This change leads to new trends in market as well as industry such as Internet

banking and e-commerce, insurance, financial transactions, supermarket, healthcare,

communications, location of data that generate huge amount of electronic data. The

need to understand huge, complex, information rich data sets is important to

virtually all fields in business, science, engineering and medical. The data used in

data warehouses and data marts has been extracted from knowledge hidden in that

data. This knowledge is becoming vital in today’s increasingly competitive world. The

greatest problem of today is how to teach people to ignore the irrelevant data.

With the rise of Machine Learning approaches we have the ability to find a solution

diabetes prediction, we have developed a system using machine learning which has

the ability to predict whether the patient has diabetes or not. Furthermore,

Diabetes Prediction


predicting the disease early leads to treating the patients before it becomes critical.

Machine Learning has the ability to extract hidden knowledge from a huge amount

of diabetes-related data. Because of that, it has a significant role in diabetes

research, now more than ever. The aim of this research is to develop a system which

can predict the diabetic risk level of a patient with a higher accuracy. This research

has focused on developing a system based on logistic regression.

1.4 Scope of the Project

The early intervention of diabetes can reduce the prevalence of diabetes and hence

the economic burden due to it. Machine Learning techniques play an important role

in treatment plan workout, rehabilitation, chronic diseases management plan etc.

Long term follow up plan may be easily guided and keen supervision is possible. The

systems may definitely helpful in reduction of cost of patient management by

avoiding unnecessary investigations and patients follow up. These prediction

systems will add accuracy and time management. Computer-based patient support

systems benefit patients by providing informational support that increases their

participation in health care.

1.5 Problem Statement and Definition

To identify whether a given person in dataset will be diabetic, non diabetic or pre-

diabetic will be done on basis of attribute values. Values exceeding a specific value

may contribute to identify whether a person is diabetic, non diabetic or pre-diabetic.

The aim of prediction of diabetes is to make aware people about diabetes and what

it takes to treat it and gives the power to control.

The model can be used by the endocrinologists, dietitians, ophthalmologists and

podiatrists to predict if or if not the patient is likely to suffer from diabetes, if yes,

how intense it could be.

The dataset consists of features comprising the medical details of the patients that

are useful in determining the health condition of the patient.

Goals of the System:-

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Convert manual to computerize. Before this, majority of the process is done

manually. After converting it to computerize it will be easy to predict.

By computerizing, it is easier to understand the doctor's report which is hardly

understood with different and complicated handwriting.

Easy to maintain record.

Enable to predict various type of diabetes.

Ensure the system useful to doctor and patient.

1.6 Objective of the study

Easy to use: The primary objective of this assignment is to broaden a

platform so as to be simple and smooth to apply, as right here one have to

provide the patient’s scientific details and primarily based on the features

extracted the algorithm will then discover diabetes and its type. As right here

set of rules does the task hence a well trained version is much less certain to

make errors in predicting diabetes and its type consequently, in short

accuracy is advanced and thereby it additionally saves time and makes

simpler for doctors in addition to sufferers to expect whether or not they

may be vulnerable to any type of diabetes or not, that is otherwise we

difficult to do without health practitioner’s involvement.

No human intervention required: To predict diabetes one must provide

scientific details which includes age, BMI, and so on. And right here the set of

rules will offer the effects based on the capabilities extracted and

consequently here probabilities of mistakes been made are very minimal

given that there is no human intervention and it also saves lot of time for the

sufferers or doctors and they could similarly continue for treatments or

different tactics should quicker. This is in case whilst consequences are

provided quicker to them. This can in-turn make the precaution/prevention

for diabetes faster while it saves medical doctors and affected person the

essential time, to be able to cross on to in addition treatments and

precautions to be taken to minimize the effect of that diabetes.

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Not simplest hit upon the diabetes kind but additionally suggest precautions:

In this mission our goal isn't only to find and are expecting the kind of

diabetes but pin point towards the precautions to be taken to minimize the

impact of the diabetes. Getting hints on precautions to be taken will help the

doctors and sufferers to progress without problems to similarly steps of their

treatment.

1.7 Existing system

The healthcare enterprise collects big quantities of healthcare facts which, unluckily,

are not “mined” to find out hidden information. Clinical decisions are frequently

made based totally on docs‟ intuition and enjoy instead of at the knowledge wealthy

statistics hidden in the database. This exercise ends in unwanted biases, errors and

excessive medical. The existing process is very slow to give the result. It is very

difficult to find diabetes or not.

1.8 Proposed system

Diabetes prediction is an internet-primarily based device gaining knowledge of

utility, skilled through a Pima Indian dataset. The person inputs its particular clinical

information to get the prediction of diabetes. The set of rules will calculate the

opportunity of presence of diabetes. Thus, minimizing the price and time required to

are expecting the disorder. Format of statistics plays essential element on this

software. At the time of uploading the user information utility will take a look at its

right record format and if it no longer as consistent with want then ERROR dialog box

may be induced. Our device might be implementing the algorithm: Logistic

Regression. The algorithms may be educated the use of the statistics set obtained

from University of California, Irvine. 75% of the entries in the statistics set can be

used for education and the last 25% for testing the accuracy of the set of rules.

Furthermore, a few steps can be taken for optimizing the algorithms thereby

enhancing the accuracy.

Advantages of the system:-

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Our proposed System has the following benefits:

Powerful, flexible, and easy to use.

Increased efficiency of doctor.

Improved patient satisfaction.

Reduce the use of papers.

Simple and Quick.

More accurate result.

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Chapter 2

Literature Survey

The Pima Indians are genetically predisposed to diabetes, and it was noted that

their diabetic rate was 19 times that of a typical town in Minnesota. The National

Institute of Diabetes and Digestive and kidney Disease of the National Institute

of Health (NIH) originally owned the Pima Indian diabetes Database (PIDD) [18].

The number of patients (n) in the database n=768 each with 9 attribute variables.

Out of the nine conditional attributes, six are due to physical examination rest of the

attributes are chemical examination. Of these 9 attributes, there are eight inputs

and last one being the output. The goal is to use the first 8 variables to predict

attribute values of the 9th variables.

The aim is to be development of a hybrid model for classifying Pima Indian

Diabetic Database. The datasets were identified and the incorrectly classified

instances were eliminated using K-means clustering. Then using decision Tree

classifier the classification is done on these correctly clustered instances. The

resultant dataset is used to train and test the diabetic data set using two method

dividing training data and test data using 60- q40 ratio and 10 fold cross

Validation method. Experimental results show the improvement in accuracy of

diabetic data set using proposed cascaded method: k-means with decision tree by

an order of 19.50% of classification compared to decision tree alone with

unprocessed data.

Weka is popular machine learning software developed in Java at University of

Waikato, New Zealand. It is open source software available at GNU (General Public

License). It consists of visualization tools and algorithms which are used in data

analysis and predictive modeling with graphical user interface for easy

functionality access. Weka supports several data mining tasks such as data pre-

processing, clustering, regression, visualization and feature selection. The

attributes available in Weka are of one of these types Nominal: one of predefined

Diabetes Prediction


list of values, Numeric: A real or integer number, String, Date, Relational. Key

features of this tool are open source and platform independent. This consists of

various algorithm for data mining and machine learning.

Diabetes a non-communicable disease is leading to long-term complications and

serious health problems. A report from the World Health Organisation addresses

diabetes and its complications that impact on individual physically, financially,

economically over the families. The survey says about 1.2 million deaths due to the

uncontrolled stage of health lead to death. About 2.2 million deaths occurred due to

the risk factors of diabetes like a cardiovascular and other diseases.

Diabetes is an ailment caused due to the extended level of sugar obsession in the

blood. In this paper, discussed various classifiers, decision support system is

proposed that uses the AdaBoost algorithm with Decision Stump as a base classifier

for classification. Moreover, Support Vector Machine, Naive Bayes and Decision Tree

have additionally executed as a base classifiers for AdaBoost calculation for

exactness confirmation. The exactness got for AdaBoost calculation with choices

stump as a base classifier is 80.72%, which is more note worthy contrasted with that

of Support Vector Machine, Naive Bayes and Decision Tree.

Artificial intelligence is having more effect is machine realizing, which creates

calculations ready to take in examples and choice standards from information.

Machine learning calculations have been implanted into information mining

pipelines, which can consolidate them with established measurable techniques, to

remove learning from information. Inside the EU-financed MOSAIC undertaking, an

information mining pipeline has been utilized to determine an arrangement of

prescient models of sort 2 diabetes mellitus (T2DM) entanglements in light of

electronic wellbeing record information of almost one thousand patients. Such

pipeline includes clinical focus profiling, prescient model focusing on, prescient

model development and model approval. In the wake of having managed to miss

information by methods for irregular woods (RF) and having connected appropriate

methodologies to deal with class unevenness, we have utilized Logistic Regression

Diabetes Prediction


with the stepwise component choice to foresee the beginning of retinopathy,

neuropathy, or nephropathy, at various time situations, at 3, 5, and 7 years from the

main visit at the Hospital Center for Diabetes (not from the conclusion). Considered

factors are sexual orientation, age, time of determination, weight file (BMI), glycated

haemoglobin (HbA1c), hypertension, and smoking propensity. Lust models, custom

fitted as per the complexities, gave an exact up to 0.838. Diverse factors were

chosen for every complexity and time situation, prompting particular models simple

to mean the clinical practice.

In this paper, analysis of a Pima Indian dataset is done using various classification

techniques like Naïve Bayes, Zero R, J48, random forest, MLP, logistic regression.

Comparison and prediction whether positive and negative diabetes. Diagnosing

diabetes through data mining tool using the WEKA tool, in terms of accuracy and

performance MLP is better.

Patients with diabetes should ceaselessly screen their blood glucose levels and

modify insulin measurements, endeavouring to keep blood glucose levels as near

typical as would be prudent. Blood glucose levels that veer off from the typical range

can prompt genuine here and now and long-haul intricacies. A programmed

expectation shows that cautioned individuals of fast approaching changes in their

blood glucose levels would empower them to make a preventive move. In this paper,

we depict an answer that uses a bland physiological model of blood glucose

progression to produce enlightening highlights for a support vector regression

display that is prepared with tolerant particular information. The new model beats

diabetes specialists at foreseeing blood glucose levels and could be utilized to

envision right around a fourth of hypoglycaemic occasions 30 min ahead of time. In

spite of the fact that the comparing exactness is right now only 42%, most false

cautions are in close hypoglycaemic locales and hence patients reacting to these

hypoglycaemia alarms would not be hurt by intercession.

Diabetes mellitus is a standout amongst the most genuine wellbeing challenges in

both creating and created nations. As per the International Diabetes Federation,

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there are 285 million diabetic individuals around the world. This aggregate is relied

upon to ascend to 380 million in 20 years. Because of its significance, an outline of a

classifier for the recognition of Diabetes ailment with ideal cost and better execution

is the need of the age. The Pima Indian diabetic database at the UCI machine

learning research facility has turned into a standard for testing information mining

calculations to see their expectation exactness in diabetes information arrangement.

The proposed strategy utilizes SVM, a machine learning technique as the classifier

for analysis of diabetes. The machine learning strategy centre around arranging

diabetes illness from a high dimensional therapeutic dataset. The trial comes about

got demonstrate that help vector machine can be effectively utilized for diagnosing

diabetes illness.

The point of this examination is to the finding of diabetes illness, which is a standout

amongst the most vital infections in the restorative field utilizing Generalized

Discriminant Analysis (GDA) and Least Square Support Vector Machine (LS-SVM).

Likewise, we proposed another course learning framework in light of Generalized

Discriminant Analysis and Least Square Support Vector Machine. The proposed

framework comprises of two phases. The primary stage, we have utilized

Generalized Discriminant Analysis to discriminant highlight factors amongst sound

and patient (diabetes) information as a pre-preparing process. The second stage, we

have utilized LS-SVM so as to order of diabetes dataset. While LS-SVM acquired

78.21% grouping precision utilizing 10-overlap. cross approval, the proposed

framework called GDA–LS-SVM got 82.05% order exactness utilizing 10-crease across

approval. The heartiness of the proposed framework is inspected utilizing

arrangement precision, k-crease cross-approval technique and disarray lattice. The

acquired order exactness is 82.05% and it is exceptionally encouraging contrasted

with the beforehand detailed grouping strategies.

A proper and thorough literature survey concludes that there are various methods

that can be used to predict diabetes. Some of these approaches are:

Artificial Neural Network

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Bayesian Network

Neural Network

Hidden Markov Method

Genetic Algorithm

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Chapter 3

System Requirements and specifications

The System Requirements Specification document describes all data, functional and

behavioral requirements of the software under production or development. A

functional requirement document defines the functionality of a system or one of its

subsystems. It also depends upon the type of software, expected users and the type

of system where the software is used. Non-functional requirement is

a requirement that specifies criteria that can be used to judge the operation of a

system, rather than specific behaviors.

3.1 General Description of the System

3.1.1 Overview of Functional requirements

Preprocess Data

Data Preprocessing is a technique that is used to convert the raw data into a clean

data set. In other words, whenever the data is gathered from different sources it is

collected in raw format which is not feasible for the analysis.

Therefore, certain steps are executed to convert the data into a small clean data set.

This technique is performed before the execution of Iterative Analysis. The set of

steps is known as Data Preprocessing. It includes

Data Cleaning

Data Integration

Data Transformation

Data Reduction

Data Preprocessing is necessary because of the presence of unformatted real-world

data. Mostly real-world data is composed of –

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Inaccurate data (missing data) - There are many reasons for missing data such as

data is not continuously collected, a mistake in data entry, technical problems with

biometrics and much more.

The presence of noisy data (erroneous data and outliers) - The reasons for the

existence of noisy data could be a technological problem of gadget that gathers data,

a human mistake during data entry and much more.

Inconsistent data - The presence of inconsistencies are due to the reasons such that

existence of duplication within data, human data entry, containing mistakes in codes

or names, i.e., violation of data constraints and much more.

Train and Test Data Creation

The data we use is usually split into training data and test data. The training set

contains a known output and the model learns on this data in order to be

generalized to other data later on. We have the test dataset (or subset) in order to

test our model’s prediction on this subset.

Figure 3.1: Training and test set

The above figure shows splitting of data into training set and test set. The size of

training set will be larger than that of test set. The training set will be trained tested

against the test set. This dataset is originally from the National Institute of Diabetes

and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically

Diabetes Prediction


predict whether or not a patient has diabetes, based on certain diagnostic

measurements included in the dataset.

Model Creation

The process of training an ML model involves providing an ML algorithm (that

is, the learning algorithm) with training data to learn from. The term ML

model refers to the model artifact that is created by the training process.

The training data must contain the correct answer, which is known as a target

or target attribute. The learning algorithm finds patterns in the training data

that map the input data attributes to the target (the answer that you want to

predict), and it outputs an ML model that captures these patterns.

You can use the ML model to get predictions on new data for which you do

not know the target. For example, let's say that you want to train an ML

model to predict if an email is spam or not spam. You would provide training

data that contains emails for which you know the target. Machine would

train an ML model by using this data, resulting in a model that attempts to

predict diabetes or not.

In our project we are using Logistic Regression to build our Model on Pima

Indian Dataset.

Result Analysis:

In this final phase, we will test our model on our prepared dataset and also measure

the Diabetes prediction performance on our dataset. To evaluate the performance of

our created classification and make it comparable to current approaches, we use

Accuracy to measure the effectiveness of classifiers.

3.1.2 Overview of Data requirements

Reliability

The structure must be reliable and strong in giving the functionalities. The

movements must be made unmistakable by the structure when a customer

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has revealed a couple of enhancements. The progressions made by the

Programmer must be Project pioneer and in addition the Test designer.

Maintainability

The system watching and upkeep should be fundamental and focus in its

approach. There should not be an excess of occupations running on diverse

machines such that it gets hard to screen whether the employments are

running without lapses.

Performance

The framework will be utilized by numerous representatives all the while.

Since the system will be encouraged on a single web server with a lone

database server outside of anyone's ability to see, execution transforms into

a significant concern. The structure should not capitulate when various

customers would use everything the while. It should allow brisk accessibility

to each and every piece of its customers. For instance, if two test specialists

are all the while attempting to report the vicinity of a bug, then there ought

not to be any irregularity at the same time.

Portability

The framework should to be effectively versatile to another framework. This

is obliged when the web server, which s facilitating the framework gets

adhered because of a few issues, which requires the framework to be taken

to another framework.

Scalability

The framework should be sufficiently adaptable to include new

functionalities at a later stage. There should be a run of the mill channel,

which can oblige the new functionalities.

Flexibility

Flexibility is the capacity of a framework to adjust to changing situations and

circumstances, and to adapt to changes to business approaches and rules. An

adaptable framework is one that is anything but difficult to reconfigure or

adjust because of diverse client and framework prerequisites. The deliberate

division of concerns between the trough and motor parts helps adaptability

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as just a little bit of the framework is influenced when strategies or principles

change.

3.2 Technical Requirements of the System

3.2.1 Hardware Requirements

System Processor : Core i3.

Hard Disk : 500 GB.

Ram : 4 GB.

Any desktop / Laptop system with above configuration or higher level.

3.2.2 Software Requirements

Operating system : Windows 8 / 10

Programming Language : Python

DL Libraries : Numpy, Pandas

3.3 Language specification

3.3.1 Python

Python is an easy to learn, powerful programming language. It has efficient high-

level data structures and a simple but effective approach to object-oriented

programming. Python’s elegant syntax and dynamic typing, together with its

interpreted nature, make it an ideal language for scripting and rapid application

development in many areas on most platforms.

The Python interpreter and the extensive standard library are freely available in

source or binary form for all major platforms from the Python Web

site, https://www.python.org/, and may be freely distributed. The same site also

https://www.python.org/

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contains distributions of and pointers to many free third party Python modules,

programs and tools, and additional documentation.

The Python interpreter is easily extended with new functions and data types

implemented in C or C++ (or other languages callable from C). Python is also suitable

as an extension language for customizable applications.

Python is a high-level, interpreted, interactive and object-oriented scripting

language. Python is designed to be highly readable. It uses English keywords

frequently where as other languages use punctuation, and it has fewer syntactical

constructions than other languages.

Python is Interpreted − Python is processed at runtime by the interpreter.

You do not need to compile your program before executing it. This is similar

to PERL and PHP.

Python is Interactive − you can actually sit at a Python prompt and interact

with the interpreter directly to write your programs.

Python is Object-Oriented − Python supports Object-Oriented style or

technique of programming that encapsulates code within objects.

Python is a Beginner's Language − Python is a great language for the

beginner-level programmers and supports the development of a wide range

of applications from simple text processing to WWW browsers to games.

3.3.2 Machine Learning Features

Machine Learning is a method of statistical learning where each instance in a

dataset is described by a set of features or attributes. In contrast, the term “Deep

Learning” is a method of statistical learning that extracts features or attributes from

raw data. Deep Learning does this by utilizing neural networks with many hidden

layers, big data, and powerful computational resources. The terms seem somewhat

interchangeable, however, with Deep Learning method, The algorithm constructs

representations of the data automatically. In contrast, data representations are

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hard-coded as a set of features in machine learning algorithms, requiring further

processes such as feature selection and extraction, (such as PCA).

Both of these terms are in dramatic contrast with another class of classical artificial

intelligence algorithms known as Rule-Based Systems where each decision is

manually programmed in such a way that it resembles a statistical model.

In Machine Learning and Deep Learning, there are many different models that fall

into two different categories, supervised and unsupervised. In unsupervised

learning, algorithms such as k-Means, hierarchical clustering, and Gaussian mixture

models attempt to learn meaningful structures in the data. Supervised learning

involves an output label associated with each instance in the dataset. This output

can be discrete/categorical or real-valued. Regression models estimate real-valued

outputs, whereas classification models estimate discrete-valued outputs. Simple

binary classification models have just two output labels, 1 (positive) and 0 (negative).

Some popular supervised learning algorithms that are considered Machine Learning:

are linear regression, logistic regression, decision trees, support vector machines,

and neural networks, as well as non-parametric models such as k-Nearest Neighbors.

3. 3. 3 Logistic Regression

Logistic regression is a statistical method for analysing a dataset in which there are

one or more independent variables that determine an outcome. The outcome is

measured with a dichotomous variable (in which there are only two possible

outcomes). It is used to predict a binary outcome (1 / 0, Yes / No, True / False) given a

set of independent variables. To represent binary / categorical outcome, we use

dummy variables. We can also think of logistic regression as a special case of linear

regression when the outcome variable is categorical, where we are using log of odds

as dependent variable. In simple words, it predicts the probability of occurrence of an

event by fitting data to a logit function.

Logistic regression was developed by statistician David Cox in 1958. This binary

logistic model is used to estimate the probability of a binary response based on one

or more predictor (or independent) variables (features). It allows one to say that the

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presence of a risk factor increases the probability of a given outcome by a specific

percentage.

Like all regression analyses, the logistic regression is a predictive analysis. Logistic

regression is used to describe data and to explain the relationship between one

dependent binary variable and one or more nominal, ordinal, interval or ratio-level

independent variables.

It’s being used in Healthcare , Social Sciences & various ML for advanced research &

analytics.

Trauma & Injury Severity Score, which is widely used to predict mortality in injured

patients, was originally developed by Boyd et al. using logistic regression. Many other

medical scales used to assess severity of a patient have been developed using logistic

regression.

Binary logistic regression is estimated using Maximum Likelihood Estimation (MLE),

unlike linear regression which uses the Ordinary Least Squares (OLS) approach. MLE is

an iterative procedure, meaning that it starts with a guess as to the best weight for

each predictor variable (that is, each coefficient in the model) and then adjusts these

coefficients repeatedly until there is no additional improvement in the ability to

predict the value of the outcome variable (either 0 or 1) for each case.

Life insurance actuaries use logistic regression to predict, based on given data on a

policy holder (e.g. age, gender, results from a physical examination) the chances that

the policy holder will die before the term of the policy expires.

Political campaigns try to predict the chances that a voter will vote for their candidate

(or do something else desirable, such as donate to the campaign).

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Chapter 4

System Design and Analysis

System Design is a process of planning a new business system or replacing an

existing system by defining its components or modules to satisfy the specific

requirements. Before planning, you need to understand the old system thoroughly

and determine how computers can best be used in order to operate efficiently.

System analysis is conducted for the purpose of studying a system or its parts in

order to identify its objectives. It is a problem solving technique that improves the

system and ensures that all the components of the system work efficiently to

accomplish their purpose.

4.1 Preliminary Design

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Figure 4.1: Preliminary design of Diabetes Prediction system

The above figure 4.1 depicts the preliminary design of Diabetes prediction system.

The system initially considers the plasma Glucose level of an individual with the age

of the person. Based on the value of Glucose level it categorizes the person whether

the person is diabetic or non diabetic. Here the design shows that the person whose

glucose level is >66.500 is considered to be diabetic and person whose glucose value

is ≤66.500 will be considered as non diabetic. If DBP is greater than 107 the patient

will be non diabetic. If the DBP is less than or equal to 107 and plasma glucose level

is greater than 28 and pregnant is considered to be diabetic otherwise non diabetic.

4.2 System Architecture

The below figure 4.1 shows the architecture of Diabetes Prediction System. The

training data set is fed to the system as input which will be initially pre processed.

Data pre processing is the phase where the raw data will be transformed into

meaningful and understandable format.

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Figure 4.2: Architecture of Diabetes Prediction System

The pre processed data will be later classified using the best classification

mechanism. Then the classified will be compared with the test data in order to

classify it accurately using some distance measures. The final classified data will be

converted to data patterns using intelligent methods. The obtained patterns will be

evaluated for accuracy and correctness. The identified patterns will be represented

as knowledge in the required form as output.

4.3 Data Flow Diagram

The data flow diagram of proposed Diabetes Prediction system is as shown in figure

4.3.

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Figure 4.3: Data Flow Diagram

The above figure 4.3 shows the Data Flow Diagram of Diabetes Prediction. The user

will enter preliminary symptoms those symptoms will be compared with symptoms

database. Symptoms will be mapped with disease. Display predicted disease with

associated symptoms after that check suitable medicine and suggest suitable

medicines from the medicine database.

4.3.1 DFD for Data Extraction

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Figure 4.4: DFD for Data Extraction

The above figure 4.4 shows the DFD for Data Extraction for the proposed system.

Raw Data will be pre processed. Processed file will be divided into 70% of training

data and 30% of test data. Model is created for the training data and save the model.

Result will be obtained for the test data using the saved model.

4.3.2 DFD for Classification of Data

Figure 4.5: DFD for Classification of Data

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The above figure 4.5 shows the DFD for Classification of Data of the proposed

system. For the problem definition data will be collected and pre processed. Model

will be created for the gathered data and data access, data sampling, data

transformation will be done. After creating the model and evaluate and interpret it.

Created model is applied for external applications.

4.3 Use Case Diagram

Figure 4.6: Use Case Diagram

The figure 4.6 above shows the use case diagram of the proposed system. Values for

each attribute assigned based on importance for the input dataset. Process and

generate value. If the value is equal to one then the patient is diabetic otherwise the

patient is non diabetic.

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Chapter 5

Implementation

In this work, a business intelligent model has been developed, to classify different

diabetes type, based on attribute values using a suitable machine learning

technique. The model was evaluated by a scientific approach to measure accuracy.

We are using Logistic Regression to build our model.

Analysis:

In this final phase, we will test our classification model on our prepared image

dataset and also measure the performance on our dataset. To evaluate the

performance of our created classification and make it comparable to current

approaches, we use accuracy to measure the effectiveness of classifiers.

After model building, knowing the power of model prediction on a new instance, is

very important issue. Once a predictive model is developed using the historical data,

one would be curious as to how the model will perform on the data that it has not

seen during the model building process. One might even try multiple model types for

the same prediction problem, and then, would like to know which model is the one

to use for the real-world decision making situation, simply by comparing them on

their prediction performance (e.g., accuracy). To measure the performance of a

predictor, there are commonly used performance metrics, such as accuracy, recall

etc. First, the most commonly used performance metrics will be described, and then

some famous estimation methodologies are explained and compared to each other.

"Performance Metrics for Predictive Modeling In classification problems, the primary

source of performance measurements is a coincidence matrix (classification matrix

or a contingency table)”. Above figure shows a coincidence matrix for a two-class

classification problem. The equations of the most commonly used metrics that can

be calculated from the coincidence matrix.

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Confusion matrix:

Figure 5.1: Confusion matrix

The above figure 5.1 shows the Confusion matrix for the proposed system. The

numbers along the diagonal from upper-left to lower-right represent the correct

decisions made, and the numbers outside this diagonal represent the errors. "The

true positive rate (also called hit rate or recall) of a classifier is estimated by dividing

the correctly classified positives (the true positive count) by the total positive count.

The false positive rate (also called a false alarm rate) of the classifier is estimated by

dividing the incorrectly classified negatives (the false negative count) by the total

negatives. The overall accuracy of a classifier is estimated by dividing the total

correctly classified positives and negatives by the total number of samples.

Numpy

Numpy, which stands for Numerical Python, is a library consisting of

multidimensional array objects and a collection of routines for processing those

arrays. Using NumPy, mathematical and logical operations on arrays can be

performed. This tutorial explains the basics of NumPy such as its architecture and

environment. It also discusses the various array functions, types of indexing, etc. An

introduction to Matplotlib is also provided. All this is explained with the help of

examples for better understanding.

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Numpy is a Python package. It stands for 'Numerical Python'. It is a library consisting

of multidimensional array objects and a collection of routines for processing of

array.

Numeric, the ancestor of NumPy, was developed by Jim Hugunin. Another package

Numarray was also developed, having some additional functionality. In 2005, Travis

Oliphant created NumPy package by incorporating the features of Numarray into

Numeric package. There are many contributors to this open source project.

Operations using NumPy

Using NumPy, a developer can perform the following operations −

Mathematical and logical operations on arrays.

Fourier transforms and routines for shape manipulation.

Operations related to linear algebra. NumPy has in-built functions for linear

algebra and random number generation.

NumPy – A Replacement for Mat Lab

NumPy is often used along with packages like SciPy (Scientific Python)

and Mat−plotlib (plotting library). This combination is widely used as a replacement

for MatLab, a popular platform for technical computing. However, Python

alternative to MatLab is now seen as a more modern and complete programming

language.

It is open source, which is an added advantage of NumPy.

The most important object defined in NumPy is an N-dimensional array type

called ndarray. It describes the collection of items of the same type. Items in the

collection can be accessed using a zero-based index.

Every item in an ndarray takes the same size of block in the memory. Each element

in ndarray is an object of data-type object (called dtype).

Any item extracted from ndarray object (by slicing) is represented by a Python

object of one of array scalar types. The following diagram shows a relationship

between ndarray, data type object (dtype) and array scalar type −

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Figure 5.2: ndarray

The above figure 5.2 shows the ndarray. An instance of ndarray class can be

constructed by different array creation routines described later in the tutorial. The

basic ndarray is created using an array function in NumPy as follows −

numpy.array

It creates an ndarray from any object exposing array interface, or from any method

that returns an array.

The ndarray objects can be saved to and loaded from the disk files. The IO functions

available are −

load() and save() functions handle /numPy binary files (with npyextension)

loadtxt() and savetxt() functions handle normal text files

NumPy introduces a simple file format for ndarray objects. This .npy file stores

data, shape, dtype and other information required to reconstruct the ndarray in a

disk file such that the array is correctly retrieved even if the file is on another

machine with different architecture.

Steps involved in predicting the heart disease:

# import standard libraries

import pandas as pd

importmatplotlib.pyplot as plt

importnumpy as np

# import libraries for prepearing model

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fromsklearn.model_selection import train_test_split

fromsklearn import metrics

# import library for Logistic regression

fromsklearn.linear_model import LogisticRegression

# load dataset

pimaDataset = pd.read_csv("diabetes.csv")

# Show the information on fields

print(pimaDataset.describe())

# Show distribution of various columns

pimaDataset.hist()

plt.show()

#split dataset into features and target variable

y = pimaDataset['Outcome']

X = pimaDataset.drop(['Outcome'], axis = 1)

# split X and y into training and testing sets

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.25,random_state=0)

# instantiate the model (using the default parameters)

logreg = LogisticRegression()

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# fit the model with data

logreg.fit(X_train,y_train)

# predict values

y_pred=logreg.predict(X_test)

# print the confusion matrix to determine model accuracy

cnf_matrix = metrics.confusion_matrix(y_test, y_pred)

print(cnf_matrix)

5.1 Different modules of the project

Training Data

Figure 5.3: Dataset

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Figure 5.3 shows the dataset used to predict the diabetes. This dataset is originally

from the National Institute of Diabetes and Digestive and Kidney Diseases. The

objective of the dataset is to diagnostically predict whether or not a patient has

diabetes, based on certain diagnostic measurements included in the dataset. Several

constraints were placed on the selection of these instances from a larger database.

In particular, all patients here are females at least 21 years old of Pima Indian

heritage. The datasets consist of several medical predictor (independent) variables

and one target (dependent) variable, Outcome. Independent variables include the

number of pregnancies the patient has had, their BMI, insulin level, age, and so on.

5.2 Flowchart of the proposed system

Figure 5.2: Flowchart of the proposed system

The above figure 5.2 shows Flowchart of the proposed system. The data from the

patient database made as training data, create model for it and train the model. New

patient details is provided it will be classified based on the details given.

Recommended score and details will be obtained.

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Chapter 6

Experimental Results

6.1 Outcomes of the Proposed System

Diabetes prediction is an internet-primarily based device gaining knowledge of

utility, skilled through a Pima Indian dataset. The person inputs its particular clinical

information to get the prediction of diabetes. The set of rules will calculate the

opportunity of presence diabetes.

Data distribution:

Figure 6.1: Data distribution

The datasets consist of several medical predictor (independent) variables and one

target (dependent) variable, Outcome. Independent variables include the number of

pregnancies the patient has had, their BMI, insulin level, age, and so on.

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Chapter 7

Testing

Software testing is defined as an activity to check whether the actual results match

the expected results and to ensure that the software system is Defect free. It

involves execution of a software component or system component to evaluate one

or more properties of interest.

7.1 Testing and Validations

Validation is a complex process with many possible variations and options, so

specifics vary from database to database, but the general outline is:

Requirement Gathering

o The Sponsor decides what the database is required to do based on

regulations, company needs, and any other important factors.

o The requirements are documented and approved.

System Testing

o Procedures to test the requirements are created and documented.

o The version of the database that will be used for validation is set up.

o The Sponsor approves the test procedures.

o The tests are performed and documented.

o Any needed changes are made. This may require another, shorter

round of testing and documentation.

System Release

o The validation documentation is finalized.

o The database is put into production.

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7.2 Testing Levels

7.2.1 Functional Testing:

This type of testing is done against the functional requirements of the project.

Types:

Unit testing: Each unit /module of the project is individually tested to check for

bugs. If any bugs found by the testing team, it is reported to the developer for fixing.

Integration testing: All the units are now integrated as one single unit and checked

for bugs. This also checks if all the modules are working properly with each other.

System testing: This testing checks for operating system compatibility. It includes

both functional and non functional requirements.

Sanity testing: It ensures change in the code doesn’t affect the working of the

project.

Smoke testing: this type of testing is a set of small tests designed for each build.

Interface testing: Testing of the interface and its proper functioning.

Regression testing: Testing the software repetitively when a new requirement is

added, when bug fixed etc.

Beta/Acceptance testing: User level testing to obtain user feedback on the product.

7.2.2 Non-Functional Testing:

This type of testing is mainly concerned with the non-functional requirements such

as performance of the system under various scenarios.

Performance testing: Checks for speed, stability and reliability of the software,

hardware or even the network of the system under test.

Compatibility testing: This type of testing checks for compatibility of the system with

different operating systems, different networks etc.

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Localization testing: This checks for the localized version of the product mainly

concerned with UI.

Security testing: Checks if the software has vulnerabilities and if any, fix them.

Reliability testing: Checks for the reliability of the software

Stress testing: This testing checks the performance of the system when it is exposed

to different stress levels.

Usability testing: Type of testing checks the easily the software is being used by the

customers.

Compliance testing: Type of testing to determine the compliance of a system with

internal or external standards.

Figure 7.1 Non-Functional Testing

The above figure 7.1 shows about Non-Functional Testing.

Reliability

The structure must be reliable and strong in giving the functionalities. The

movements must be made unmistakable by the structure when a customer

has revealed a couple of enhancements. The progressions made by the

Programmer must be Project pioneer and in addition the Test designer.

Maintainability

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The system watching and upkeep should be fundamental and focus in its

approach. There should not be an excess of occupations running on diverse

machines such that it gets hard to screen whether the employments are

running without lapses.

Performance

The framework will be utilized by numerous representatives all the while.

Since the system will be encouraged on a single web server with a lone

database server outside of anyone's ability to see, execution transforms into

a significant concern. The structure should not capitulate when various

customers would use everything the while. It should allow brisk accessibility

to each and every piece of its customers. For instance, if two test specialists

are all the while attempting to report the vicinity of a bug, then there ought

not to be any irregularity at the same time.

Portability

The framework should to be effectively versatile to another framework. This

is obliged when the web server, which s facilitating the framework gets

adhered because of a few issues, which requires the framework to be taken

to another framework.

Scalability

The framework should be sufficiently adaptable to include new

functionalities at a later stage. There should be a run of the mill channel,

which can oblige the new functionalities.

Flexibility

Flexibility is the capacity of a framework to adjust to changing situations and

circumstances, and to adapt to changes to business approaches and rules. An

adaptable framework is one that is anything but difficult to reconfigure.

7.3 White Box Testing

White Box Testing is defined as the testing of a software solution's internal structure,

design, and coding. In this type of testing, the code is visible to the tester. It focuses

primarily on verifying the flow of inputs and outputs through the application,

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improving design and usability, strengthening security. White box testing is also

known as Clear Box testing, Open Box testing, Structural testing, Transparent Box

testing, Code-Based testing, and Glass Box testing. It is usually performed by

developers.

It is one of two parts of the "Box Testing" approach to software testing. Its

counterpart, Blackbox testing, involves testing from an external or end-user type

perspective. On the other hand, Whitebox testing is based on the inner workings of

an application and revolves around internal testing.

The term "WhiteBox" was used because of the see-through box concept. The clear

box or WhiteBox name symbolizes the ability to see through the software's outer

shell (or "box") into its inner workings. Likewise, the "black box" in "Black Box

Testing" symbolizes not being able to see the inner workings of the software so that

only the end-user experience can be tested.

Internal security holes

Broken or poorly structured paths in the coding processes

The flow of specific inputs through the code

Expected output

The functionality of conditional loops

Testing of each statement, object, and function on an individual basis

The testing can be done at system, integration and unit levels of software

development. One of the basic goals of whitebox testing is to verify a working flow

for an application. It involves testing a series of predefined inputs against expected

or desired outputs so that when a specific input does not result in the expected

output, you have encountered a bug.

7.4 Different Stages of Testing

7.4.1 Unit Testing

https://www.guru99.com/black-box-testing.html



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Unit Testing is a level of software testing where individual units/ components of a

software are tested. The purpose is to validate that each unit of the software

performs as designed. A unit is the smallest testable part of any software. It usually

has one or a few inputs and usually a single output. In procedural programming, a

unit may be an individual program, function, procedure, etc. In object-oriented

programming, the smallest unit is a method, which may belong to a base/ super

class, abstract class or derived/ child class. (Some treat a module of an application as

a unit. This is to be discouraged as there will probably be many individual units

within that module.) Unit testing frameworks, drivers, stubs, and mock/ fake objects

are used to assist in unit testing.

Unit Test Plan:

Unit Test Plan

o Prepare

o Review

o Rework

o Baseline

Unit Test Cases/Scripts

o Prepare

o Review

o Rework

o Baseline

Unit Test

o Perform

Benefits

Unit testing increases confidence in changing/ maintaining code. If good unit

tests are written and if they are run every time any code is changed, we will

be able to promptly catch any defects introduced due to the change. Also, if

codes are already made less interdependent to make unit testing possible,

the unintended impact of changes to any code is less.

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Codes are more reusable. In order to make unit testing possible, codes need

to be modular. This means that codes are easier to reuse.

Development is faster. How? If you do not have unit testing in place, you

write your code and perform that fuzzy ‘developer test’ (You set some

breakpoints, fire up the GUI, provide a few inputs that hopefully hit your

code and hope that you are all set.) But, if you have unit testing in place, you

write the test, write the code and run the test. Writing tests takes time but

the time is compensated by the less amount of time it takes to run the tests;

You need not fire up the GUI and provide all those inputs. And, of course, unit

tests are more reliable than ‘developer tests’. Development is faster in the

long run too. How? The effort required to find and fix defects found during

unit testing is very less in comparison to the effort required to fix

defects found during system testing or acceptance testing.

The cost of fixing a defect detected during unit testing is lesser in comparison

to that of defects detected at higher levels. Compare the cost (time, effort,

destruction, humiliation) of a defect detected during acceptance testing or

when the software is live.

Debugging is easy. When a test fails, only the latest changes need to be

debugged. With testing at higher levels, changes made over the span of

several days/weeks/months need to be scanned.

7.4.2 Integration Testing

Integration Testing is a level of software testing where individual units are combined

and tested as a group. The purpose of this level of testing is to expose faults in the

interaction between integrated units. Test drivers and test stubs are used to assist in

Integration Testing.

integration testing: Testing performed to expose defects in the interfaces

and in the interactions between integrated components or systems. See also

component integration testing, system integration testing.

component integration testing: Testing performed to expose defects in the

interfaces and interaction between integrated components.

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system integration testing: Testing the integration of systems and packages;

testing interfaces to external organizations (e.g. Electronic Data Interchange,

Internet).

Tasks

Integration Test Plan

o Prepare

o Review

o Rework

o Baseline

Integration Test Cases/Scripts

o Prepare

o Review

o Rework

o Baseline

Integration Test

7.4.3 System Testing

System Testing is a level of software testing where a complete and integrated

software is tested. The purpose of this test is to evaluate the system’s compliance

with the specified requirements. The process of testing an integrated system to

verify that it meets specified requirements.

7.4.4 Acceptance Testing

Acceptance Testing is a level of software testing where a system is tested for

acceptability. The purpose of this test is to evaluate the system’s compliance with

the business requirements and assess whether it is acceptable for delivery. Formal

testing with respect to user needs, requirements, and business processes conducted

to determine whether or not a system satisfies the acceptance criteria and to enable

the user, customers or other authorized entity to determine whether or not

to accept the system.

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Chapter 8

Conclusion and Future Enhancement

Project introduction is the first step in building a system. Basically it will tell what is

the application or a system that we are intended to build, what it will look like, brief

describe on the proposed project, setting up the project scope, defining project

objective, problem statements of the project and also the expected outcome. This

stage will be used as a reference to ensure system meet the project scope and

project objective.

8.1 Conclusion

Diabetes is vital health hassle in human society. This paper has summarised kingdom

of art techniques and to be had techniques for predication of this sickness. Deep

studying an rising region of Machine Learning showed a few promising bring about

different area of clinical diagnose with excessive accuracy. It continues to be an open

area waiting to get applied in Diabetes predication. Some strategies of deep studying

has been discussed which may be implemented for Diabetes predication, alongside

pioneer machine getting to know algorithms. An analytical assessment has been

completed for locating out best available algorithm for clinical dataset. In future our

purpose is to carry ahead the work of temporal scientific dataset, wherein dataset

varies with time and retraining of dataset is needed.

8.2 Future Enhancement

The proposed system is Diabetes prediction. We can enhance this system should

be efficient to predict the diseases and suggestion of medications using machine

learning. Further the system can be extended to N number diseases existing with

proper medications.

LIMITATIONS:

The size of the database increases day-by-day, increasing the load on the

database back update data maintenance activity.

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Training for simple computer operations is necessary for the users working on the

system.

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