DISEASE PREDICTION WITH THE HELP OF SYMPTOMS

USING DATA MINING

Project submitted to the

Thassim Beevi Abdul Kader College for Women

for the partial fulfillment of the requirements for the award of the degree of

Master of Computer Applications

by

Rahumath Rahima Beevi J

21MCA0014

Under the Supervision of

Dr. P. Senthil Kumari

DEPARTMENT OF COMPUTER SCIENCE

AND RESEARCH CENTER
THASSIM BEEVI ABDUL KADER COLLEGE FOR WOMEN
(An Autonomous Institution)
KILAKARAI – 623517, TAMIL NADU, INDIA.

April 2023

i
 CERTIFICATE

This is to certify that the work contained in the project entitled “Disease Prediction
with the help of Symptoms Using Data Mining”, submitted by Rahumath Rahima Beevi J
(Reg No: 21MCA0014) for the award of the degree of Master of Computer Applications to
the Thassim Beevi Abdul Kader College for Women, Kilakarai is a record of bonafide
research work carried out by her under my direct supervision.

I considered that the project has reached the standards and fulfilling the
requirements of the rules and regulations relating to the nature of the degree. The contents
embodied in the project have not been submitted for the award of any other degree or diploma
in this or any other university / college.

Date: 11/04/2023.
Place: Kilakarai.

Signature of the Supervisor

Dr. P. Senthil Kumari,
Head In - Charge,
Department of Computer Science
and Research Center.
Thassim Beevi Abdul Kader College for Women.

Approved by:

Head of the Department

Internal Examiner External Examiner

ii
 DECLARATION

I certify that

a. The work contained in the project is original and has been done by myself under the
supervision of my supervisor.

b. The work has not been submitted to any other Institute for any degree or diploma.

c. I have conformed to the norms and guidelines given in the Ethical Code of Conduct of
the Institute.

d. Whenever I have used materials (data, theoretical analysis, and text) from other sources,
I have given due credit to them by citing them in the text of the project and giving their
details in the references.

e. Whenever I have quoted written materials from other sources and due credit is given to
the sources by citing them.

f. From the plagiarism test, it is found that the similarity index of whole project within
25% and single paper is less than 10 % as per the university guidelines.

Date: 11/04/2023.

Place: Kilakarai.

Rahumath Rahima Beevi J

21MCA0014

iii
 ACKNOWLEDGEMENT

I take this opportunity to express my sincere and heartfelt thanks to Dr. S. Sumaya,
Principal, Thassim Beevi Abdul Kader College for Women, Kilakarai and Dr. M. S. Irfan
Ahmed, Director – Research of our institute for providing the necessary infrastructures,
suggestion and facilities to carry out this project work.

I extend my sincere thanks to Dr. K. Bavya Devi, Research Head, Research and
Development Cell of our institute and Dr. P. Senthil Kumari, Head In-Charge, Department of
Computer of Science and Research Center, Thassim Beevi Abdul Kader College for Women,
Kilakarai for their moral supports.

I record my deep sense of gratitude to my research supervisor Dr. P. Senthil Kumari,

Head In-Charge, Department of Computer Science and Research Center, Thassim Beevi Abdul
Kader College for Women, Kilakarai. It is not possible to describe in words the way she lifted
my spirits through her guidance at the time of research. I could not have asked for more than
what she has given me in the form of guidance and constant encouragement.

I also thank my review panel members Dr. R. Punitha, Ms. G. Saravana Priya & Ms.
R. Rajeshwari for their valuable suggestions and comments during my review seminars.

I extend my gratitude to all my Classmates and Friends for their various helps and
encouragement at the right time.

The words cannot be sufficient to acknowledge the contribution of my lovable mother

Mrs. A. Syed Ali Fathima who will always remain the source of inspiration for me.

Rahumath Rahima Beevi J

iv
 ABSTRACT

The Project “Disease Prediction with the help of Symptoms Using Data Mining”
is an end user support and online consultation system. Here I propose a system that allows
users to get instant guidance on their health issues through an intelligent health care system
online. The system is fed with various symptoms and the disease/illness associated with
those systems. The system allows user to share their symptoms and issues. It then processes
user’s symptoms to check for various illnesses that could be associated with it. The proposed
system includes a prediction system that patient can get consultations from various doctors
for their health issues. In the medical area, decisions usually have very high threat and to
avoid that threat result should be very precise with confirmation from doctors. In this
proposed system, Here, I use some kind of intelligent data mining techniques and to guess
the most accurate illness that could be associated with patient’s symptoms. A major objective
is to evaluate data mining techniques in clinical and health care applications to develop
accurate decisions. It also gives a detailed discussion of medical data mining techniques
which can improve various aspects of Clinical Predictions. It is a new powerful technology
which is of high interest in computer world. Admin module can easily add the specific type
of diseases which is presented and then doctor module also be presented in the system. Based
on the disease and symptom the data mining algorithm works. Here I use a proper guidance
when the user specification symptoms of his illness.

Keywords: Prediction – Disease – Symptoms – Data Mining– Decisions - Intelligent.

v
 LIST OF ABBREVIATIONS

Abbreviation Description
ASP.NET Active Server Page Dot Net

API Application Programming Interface

BCL Base Class Library

CLI Common Language Infrastructure

CLR Common Language Runtime

CTS Common Type System

DBMS Database Management System

DFD Data Flow Diagram

IIS Internet Information Services

NGWS Next Generation Windows Services

SQL Structured Query Language

UML Unified Modeling Language

UI User Interface

WPF Windows Presentation Foundation

WCF Windows Communication Foundation

WWF Windows Work Flow Foundation

WCS Windows Card Space

vi
 LIST OF FIGURES

Figure No. Figure Description Page No.

Chapter 3

SYSTEM ARCHITECTURE
3.1 Work Flow of Disease Prediction (Admin Side) 13
3.2 Work Flow of Disease Prediction (Doctor Side) 14
3.3 Work Flow of Disease Prediction (Patient Side) 15
3.4 UML Diagram of Disease Prediction 16
16
3.5 Use case Diagram of Disease Prediction
3.6 Sequence Diagram of Disease Prediction 17
3.7 Class Diagram of Disease Prediction 18

DATA FLOW DIAGRAM

3.1 Data Flow Diagram of Doctor’s Registration
20
3.2 Data Flow Diagram of Patient’s Registration
3.3 Data Flow Diagram of Admin Login 21

MODULES
3.1 Doctor’s Registration 24
3.2 Patient’s Registration
25
3.3 Admin Login
3.4 Admin Page
26
3.5 Doctor Page
3.6 Patient Page 27
3.7 Disease Prediction
28
3.8 Appointments

Chapter 4
DISEASE PREDICTION RESULTS
4.1 Search Disease 29
4.2 Predicted Disease 30

vii
 LIST OF TABLES

Table No. Table Description Page No.

Chapter 3
DATA BASE DESIGN
3.1 Districts
9
3.2 Specialization
3.3 Doctor Registration
10
3.4 Patient Registration
3.5 Disease Prediction 11
3.6 Appointments 12

viii
 CONTENTS

Content Details Page No.

Title Page i
Certificate by the Supervisor ii
Declaration iii
Acknowledgements iv
Abstract v
List of Abbreviations vi
List of Figures vii
List of Tables viii
Contents ix
Chapter 1 Introduction

1.1 About the Project 1

1.2 Problem Statement of the Project 2
1.3 Objectives of the Project 3
1.4 Organization of the Project 4

Chapter 2 Literature Survey

2.1 Review of Literature

2.1.1 Prediction Literature Works
2.1.2 Heart Disease Literature Works 5
2.1.3 Chronic Disease Literature Works
2.1.4 Disease Framework Literature Works

Chapter 3 Implementation and Methodology Used

3.1 Implementation 6
3.1.1 Front End - C# 7

ix
 3.1.2 Back End - SQL Server
3.1.3 Framework – ASP.NET 8
3.1.4 DataBase Design
3.2 System Architecture 12

3.2.1 Work Flow 13

3.2.2 UML Diagram 15

16
3.2.3 Use case Diagram

3.2.4 Sequence Diagram

17
3.2.5 Class Diagram 18
19
3.2.6 Data flow Diagram

3.3 Apriori Algorithm 21

3.3.1 Existing System
3.3.2 Proposed System 22
3.4 Methodology Used
3.4.1 Modules 23
3.4.2 Modules Description 24

Chapter 4 Results and Discussion

29
4.1 Disease Prediction Results

Chapter 5 Conclusion and Future Enhancement

5.1 Conclusion 31

5.2 Future Enhancement

5.3 Appendices 32

5.3.1 Coding

5.4 Screen Shots 56

5.5 References 65

x
 CHAPTER 1

1. INTRODUCTION

1.1 ABOUT THE PROJECT

Disease Prediction with the help of symptoms using Data Mining is a system which
primarily works according to the symptoms given by a user. The disease is predicted using
algorithms and of the datasets with the symptoms provided by the user. There are currently a
lot of health institutions that has been developed such as hospitals and medical centers which
are crucial to maintain and improve the health of the community around us. It is a prime
establishment of giving proper health care especially for every one of us who have ever lived.
For every illness and disease that people may face today and sometime in the future, it is
because of these medical institutions and all the doctors who worked at these places that have
made our lives physically better and healthy. With all of these issues mentioned, solutions are
needed to be made and that is where a health prediction system should be implemented which
could potentially eliminate these concerning issues. Hence, the current research proposes to
apply data mining for smart health prediction.

DATA MINING

Data mining can be described as a process of searching patterns or correlations from

large data sets to valuable information that can solve problems and predict outcomes. It
involves analyzing certain amount of information to locate certain patterns of occurrence to
predict future tendencies, using several processes of effective data collection, warehousing
and computer processing. With this functionality therefore, it serves a great purpose when it
comes to predict people’s health diseases especially on finding correlation between the health
information that has been given by both the medical staff and the patient. These finds may
provide a beneficial advantage in the healthcare industry as it may be used to manage patients
on their current health issues and for doctor to alleviate them from their jobs. It is a prime
establishment of giving proper health care especially for every one of us who have ever lived.
Although hospitals now are well-equipped with their staff working, there are still known
issues that still persists that because the staff to make poor clinical decision that affects a
patient’s health such as the lack of qualified doctors, unorganized health information and
poor communications between doctors and patients.

1
DISEASES PREDICTED

The health industry has been growing a lot from past few years. This technique has
gained a lot of importance in medical areas. It has been calculated that a care hospital may
generate five terabytes of data in the year. In our day to day life we have lot of other problems
to deal with and we neglect our health problems. So in order to overcome such problem we
have designed user friendly website which helps users to get diagnosed from their residence at
any time. Data mining has variety of scopes in major fields some of which are listed below:

• Administration of health services

• Clinical care

• Medical research

• Training

To analyze large amount of data, data mining technique is used. For each subfield of
Clinical Predictions, and also presented how clinical data warehousing in combination with
data mining can help administrative, clinical, research and educational aspects of Clinical
Predictions.

1.2 PROBLEM STATEMENT OF THE PROJECT

• The proposed system suggested will predict the disease based on the symptoms and
severity rating entered by the user and by considering user’s previous medical history
to predict the possible disease and also provide them with homemade remedies to give
some instant relief to the user.
• In the proposed work, I try to overcome this barrier by introducing a generalized
approach of disease prediction where several common and frequent disease that
happen to humans in daily life are predicted.
• A method for identifying frequency of diseases in particular geographical
location for a given period of time using Apriori data mining technique based
on association rules is proposed.
• This system tends to replace the existing system for going to the doctor for getting
diagnosis on illness you are suffering from to a smart solution where user get instant
diagnosis entering symptoms in the system.

2
• In my proposed system I use data mining method where user entered their symptoms.

• Symptoms are cross checked in the database and frequent item sets are mined out of
the existing database.
• The proposed system is an efficient algorithm implemented for the diagnosis of
diseases.

1.3 OBJECTIVES OF THE PROJECT

 There is a need to study and make a system which will make it easy for end users to
predict the diseases without visiting physician or doctor for diagnosis.
 To detect the Various Diseases through the examining Symptoms of patient's using
different techniques.
 There are currently a lot of health institutions that has been developed such as
hospitals and medical centers which are crucial to maintain and improve the health of
the community around us.
 We also provide an option for booking an appointment with the doctor to discuss
health related problems and get diagnosed properly.

1.4 ORGANIZATION OF THE PROJECT

 This project aims to predict the disease on the basis of the symptoms. The project
is designed in such a way that the system takes symptoms from the user as input and
produces output to predict disease.
 In Chapter 1 describes about the entire project of Disease Prediction with the help of
symptoms using Data Mining. Here, I proposed the Data Mining Techniques with the
help of Apriori Algorithms to relate the objects between them by using the association
rules.
 In Chapter 2 Review of Literature fully describes about the research papers of
author’s perspective and deeply analyze the new kind of proposed system.
 In Chapter 3 describes about the Implementation and methodology used. Here, I used
new kind of implementation in my projects and describes the modules of each steps.

3
 In Chapter 4 describes about the Results and Discussion about the projects, after
verifying the each segments of the projects, it will be easily analysing the predicting
the disease.
 In Chapter 5 describes about the Conclusion and Future Enhancement has been
processed by scope of my project.

4
 CHAPTER 2
2. LITERATURE SURVEY

2.1 REVIEW OF LITERATURE

2.1.1 PREDICTION LITERATURE WORKS

In the paper “Smart health prediction system using data mining”[1] the author has
discussed many topics related to data mining techniques such as Naive Bayes, KDD
(Knowledge discovery in Database).
The Bayesian statistics can be applied to economic sociology and other fields. The
paper “A Smart Health Prediction Using Data Mining” [2] is explaining the similar topics to
the paper [1].

2.1.2 HEART DISEASE LITERATURE WORKS

In the Paper [3] “Heart Disease Prediction Using Effective Machine Learning
Techniques”, the author has discussed about the patients symptoms caused due to his illness.

2.1.3 CHRONIC DISEASE LITERATURE WORKS

In the paper “Chronic disease risk prediction using distributed machine learning
classifiers.” [3] Here, the author explained detailed of the internal algorithms used in the
system. Most of the topics covered are on the system architecture.
In this paper the design aspects of the system are primarily focused. In this paper [4]
the author has given a detailed framework to beat the downside of existing system.

2.1.4 DISEASE FRAMEWORK LITERATURE WORKS

In the paper [5] the author applies the data mining process to predict hypertension
from patient medical records with eight other diseases. And the Smart Health Framework is
used to implement the design aspects of the project.
In the paper [6] the author said that data mining can be applied on health data for a
wide range of purposes and examinations. Adequately coordinating and productively examine
different types of Healthcare data over some stretch of time can answer a large number of
approaching Healthcare issues.
In this Paper [7] author says that Information mining strategies are frequently used to
characterize whether a patient is typical or having coronary illness.

5
 CHAPTER 3

3. IMPLEMENTATION AND METHODOLOGY USED

3.1 IMPLEMENTATION

Each program is tested individually at the time of development using the data and has
verified that this program linked together in the way specified in the program specification
the computer system and its environment is tested to the satisfaction of the user. The system
that has been developed is accepted and proved to be satisfactory for the user. A simple
operating procedure is included so that the user can understand the different function clearly
and quickly. Initially as a first step the executable form of the application is to be created and
loaded in the common server machine which is accessible to the entire user and the server is
to be connected to a network. The final stage is to document the entire system which is
provides components and the operating procedure of the system. Here, I use Apriori
algorithm is one of the most basic and popular algorithms for association rules mining.
Apriori is designed to operate on databases containing the objects between relationships of
association rules. In the Apriori algorithm, every transaction is seen as itemsets, with a given
threshold, the algorithm will identify the item, which is subset at least by minimum threshold
as new items.
The proposed work is implemented Intel(R) Core(TM) 1.80 GHz processor, 512 MB
RAM, 40 GB HDD in a hardware requirements. Here I use ASP.NET framework and to make
C# programming language as the entire frontend. The middle tier or code behind model is
designed in HTML. And sql serves as a backend to store data. Implementation is the most
curial stage in archiving a successful system and giving the user confidence that the new
system is workable and effective. Implementation of a modified application to replace an
existing one this type of conversion is relatively easy to handle, provide there are no major
changes in the system.

6
3.1.1 FRONT END - C#

Here, I Propose C# programming language encompassing strong typing, imperative,

declarative, functional, generic, object-oriented (class-based) and component-oriented
programming disciplines. It was developed by Microsoft within its .Net initiative and later
approves as a standard by ECMA and ISO. C# is one of the programming languages designed
for the Common Language Infrastructure
A hybrid of C and C++, It is a Microsoft programming language developed to
compete with Sun's Java language. C# is an object-oriented programming language used with
XML-based Web services on the.NET platform and designed for improving productivity in
the development of Web applications. C# boasts type-safety, garbage collection, simplified
type declarations, versioning and scalability support, and other features that make developing
solutions faster and easier, especially for COM+ and Web services. Microsoft critics have
pointed to the similarities between C# and Java.

3.1.2 BACK END - SQL SERVER

Here, I Propose the SQL (Structured Query Language) is a special-purpose

programming language designed for managing data held in a relational database management
system (RDBMS). Microsoft SQL Server is a relational database management system
developed by Microsoft. As a database, it is a software product whose primary function is to
store and retrieve data as requested by other software applications, be it those on the same
computer or those running on another computer across a network (including the Internet).
Generically, any Database Management System (DBMS) that can respond to queries from
client machines formatted in the SQL language. When capitalized, the term generally refers to
either of two database management products from Sybase and Microsoft.
Both companies offer client-server DBMS products called SQL Server.

7
3.1.3 FRAMEWORK – ASP.NET

Here, I Propose the ASP.NET framework for building great websites and web
applications using HTML, CSS etc. It can also create Web APIs and use real-time
technologies like Web Sockets. ASP.NET Core is an alternative to ASP.NET.

ADVANTAGES

• Speed. With ASP.NET, you can develop a website with relative quickness. This is
because of the many beneficial features of the language.
• Rich controls. ASP.NET comes with the huge collection of rich server and client side
controls that you can use to develop interactive grids, wizards, calendars, etc.
• Security. ASP.NET comes with built-in security that allows support for authorization,
authentication and other options for implementation with Kerberos, NTLM as well
other standard.

3.1.4 DATABASE DESIGN

 Database Design is the organization of data according to Database model.

 The designer determines what data must be stored and how the data elements
interrelate.
 With this information, they can begin to fit the data to the database model.
 A database management system manages the data accordingly.
 Here, a list of Tables is mentioned as Columns name and Data Types.
 Here, I propose the 6 major tables, they are: Districts, Specialization, Doctor
Registration, Patient Registration, Disease Prediction, and Appointments.
 In each Table, we can easily add the item of the datasets and give the each datatype
name.

8
DISTRICTS

Column name Data type

Dist Id Int

District Varchar(50)

Table 3.1: Districts

In the above table is mentioned as districts. In this table you can easily add the district id and
district name and to check the doctor as nearest place. Here, I includes Column name and
Data type, and I create an Dist Id, Dist name as Column names and the data type is mentioned
as the Dist Id as Integer type and district name as Varchar.

SPECIALIZATION

Column name Data type

Sp Id Int

Specialization Varchar(50)

Table 3.2: Specialization

In the above table is mentioned as specialization. In this table you can easily identify the
doctors specialization and to consult them easily. Here, I includes Column name and Data
type, and I create a specialization id and its name as Column names and the data type is
mentioned as the Specialization Id as Integer type and specialization name as Varchar.

9
DOCTOR REGISTRATION

Column name Data type

Reg no Varchar(50)
Doctor name Varchar(50)
District Varchar(50)
Addr Varchar(max)
Email Varchar(50)
Contact Varchar(50)
Specialization Varchar(50)
Uname Varchar(50)
Pwd Varchar(50)

Table 3.3: Doctor Registration

In the above table is mentioned as Doctor’s Registration. In this table doctors can be easily
registered their id, name, district, address, email, contact, specialization, username and
password. After their registration process is completed. Doctors Username and Password is
automatically generated to the registered doctors. So that Registered Doctors can be easily
login to the doctor’s portal. Here, I include Column name and Data type for the Doctor’s
Registration process.

PATIENT REGISTRATION

Column name Data type

Patient Id Varchar(50)
Nam Varchar(50)
District Varchar(50)
Addr Varchar(50)
Email Varchar(50)
Contact Varchar(50)
Unam Varchar(50)
Pwd Varchar(50)

Table 3.4: Patient Registration

10
In the above table is mentioned as Patient’s Registration. In this table Patients can be easily
registered their id, name, district, address, email, contact, specialization, username and
password. After their registration process is completed. Patients Username and Password is
automatically generated to the registered doctors. So that Registered Doctors can be easily
login to the doctor’s portal. Here, I include Column name and Data type for the Patient’s
Registration process.

DISEASE PREDICTION

Column name Data type

Disease Id Int

Disease Varchar(50)

Symptom Varchar(max)

Type Varchar(50)

Table 3.5: Disease Prediction

In the above table is mentioned as Disease. In this table doctors can be easily Predicted their
Diseases by using several of Symptoms. By considering the symptoms of each diseases and
their types has been predicted. Here, I mentioned as disease id, their symptoms and its types.
After identifying the diseases it will be automatically predicted. Here, I include Column name
and Data type for the Disease Prediction process.

11
APPOINTMENTS

Column name Data type

Patient Id Varchar(50)
Patient Name Varchar(50)
Doctor name Varchar(50)
Place Varchar(50)
Date Date time
Time app Date time

Table 3.6: Appointments

In the above table is mentioned as Appointment. In this table Patient’s verify the nearest
doctor for predicted their diseases and booked an appointment for the available doctor. So
that, the notification will be generated to the appointed doctor’s page and then doctor can be
easily identify the patient’s message and then confirm the appointment to diagnosis the
patient. Here, I include Column name and Data type for booking appointments.

3.2 SYSTEM ARCHITECTURE

System architecture is the computational design that defines the structure and or
behaviour of a system. An architecture description is a formal description of a system.
Organized in a way that supports reasoning about the structural properties of the system. It
defines the system components or building blocks provides a plan from which products can
be procured, and systems developed, that will work together to implement the overall
system. In this architectural structure, I briefly explained about the system work flow of
disease prediction, UML Diagram, Use Case Diagram, Sequence Diagram and Class
Diagram.

12
3.2.1 WORK FLOW

 According to the diagrams, It includes three work flow. Here, I provide a form that
shows a list of symptoms. The user will input those symptoms that experiences.
 On the basis of selected symptoms the system will generate related disease. The
system will show another form that contain some queries if the information for the
disease is not enough.
 On the basics of the information a query is generated and the data base responses to
that query.

Figure 3.1: Work Flow of Disease Prediction (Admin Side)

13
 In the above the Figure 3.1 is mentioned as Work Flow of Admin, The Admin work is
to perform an Add the Doctor, View the Doctor, Add the Disease and View the Disease of my
System. Admin can add disease details along with symptoms and type. Admin can view
feedback provided by various users.

Figure 3.2: Work Flow of Disease Prediction (Doctor Side)

In the above the Figure 3.2 is mentioned as Work Flow of Doctor, The Doctor work is
to perform an analyzing of symptoms, Doctor will access the system using his / her User ID
and Password. Doctor can view Patient’s Personal Records of affected diseases and verify
them to predicting the diseases.

14
 Figure 3.3: Work Flow of Disease Prediction (Patient Side)

In the above the Figure 3.3 is mentioned as Work Flow of Patient, Patient’s work is to
login to the system using his ID and Password. Patient will specify the symptoms caused due
to his illness. Patient can search for doctor by specifying name, address or type.

3.2.2 UML DIAGRAM

UML stands for Unified Modeling Language. UML is a standardized general-purpose

modelling language in the field of object-oriented software engineering. The Standard is
managed, and was created by, the Object Management Group. The goal is for UML to
become a common language for creating models of object oriented computer software. In its
current form UML is comprised of two major components: a Meta-model and a notation. In
the future, some form of method or process may also be added to; or associated with, UML.
The UML represents a collection of best engineering practices that have proven successful in
the modelling of large and complex systems.

15
 Figure 3.4: UML Diagram of Disease Prediction

3.2.3 USE CASE DIAGRAM

A use case diagram in the Unified Modeling Language (UML) is a type of behavioral
diagram defined by and created from a Use-case analysis. Its purpose is to present a graphical
overview of the functionality provided by a system in terms of actors, their goals (represented
as use cases), and any dependencies between those use cases. The main purpose of a use case
diagram is to show what system functions are performed for which actor. Roles of the actors
in the system can be depicted.

Login

Disease
Prediction

Register

View disease

Add

Admin Book User

View

View

Figure 3.5: Use Case Diagram of Disease Prediction

16
 3.2.4 SEQUENCE DIAGRAM

A sequence diagram in Unified Modeling Language (UML) is a kind of interaction

diagram that shows how processes operate with one another and in what order. It is a
construct of a Message Sequence Chart. Sequence diagrams are sometimes called event
diagrams, event scenarios, and timing diagrams.

Figure 3.6: Sequence Diagram of Disease Prediction

17
 3.2.5 CLASS DIAGRAM

In software engineering, a class diagram in the Unified Modeling Language (UML)

is a type of static structure diagram that describes the structure of a system by showing the
system's classes, their attributes, operations (or methods), and the relationships among the
classes. It explains which class contains information.
….

User
+username
+password
+login
+view doctors
Register
+id
+username
+password
+gender
+address Admin
+username
+register() +password
+login
+viewdoctors
+viewusers

Figure 3.7: Class Diagram of Disease Prediction

18
 3.2.6 DATA FLOW DIAGRAM

• The DFD is also called as bubble chart. It is a simple graphical formalism that can be
used to represent a system in terms of the input data to the system, various processing
carried out on these data, and the output data is generated by the system
• The Data Flow Diagram (DFD) is one of the most important modelling tools. It is
used to model the system components. These components are the system process, the
data used by the process, an external entity that interacts with the system and the
information flows in the system.
• DFD shows how the information moves through the system and how it is modified by
a series of transformations. It is a graphical technique that depicts information flow
and the transformations that are applied as data moves from input to output.
• DFD is also known as bubble chart. A DFD may be used to represent a system at any
level of abstraction. DFD may be partitioned into levels that represent increasing
information flow and functional detail.

NOTATION SOURCE OR DESTINATION OF DATA

External sources or destinations, which may be people or organizations or other

entities.

DATA SOURCE

Here the data referenced by a process is stored and retrieved.

PROCESS

People, procedures or devices that produce data. The physical component is not
identified.

19
 DATA FLOW

Data moves in a specific direction from an origin to a destination. The data flow is a
“Packet” of data.

DATA FLOW DIAGRAM

DOCTOR REGISTRATION

Doctor can register them and login to the system to view the appointments.

Registered Add
Login Data base
Doctor details &
Government
Registration View
Number

Figure 3.1: Data Flow Diagram of Doctor Registration

PATIENT REGISTRATION

Patient can login to the system and they can search for doctor by specifying name,
address or type.

Registered Add
Login Data base
Patient details &
User id,
Password View

Figure 3.2: Data Flow Diagram of Patient Registration

20
ADMIN LOGIN

Admin can login to the system using their ID and Password to perform their work and
to add the details of the symptoms and types then added to provide the feedback by the users.

Login Add the

Admin disease details
along with
symptom and
type

ADD View

Provide the
feedback by Data base
the users

Figure 3.3: Data Flow Diagram of Admin Login

3.3 ALGORITHM USED

• Here, I used Apriori algorithm which is used to perform the association rules
between objects. It means how two or more objects are related to one another.
• In other words, we can say that the Apriori algorithm is an association rule leaning
that analyzes that people who are all suffer from diseases.
• The primary objective of the Apriori algorithm is to create the association rule
between different objects. The association rule describes how two or more objects
are related to one another.
• Apriori algorithm is also called frequent pattern mining. Generally, we operate the
Apriori algorithm on a database that consists of a huge number of transactions.

21
 • Let's understand the Apriori algorithm with the help of an example; in this disease
prediction methodology, it can be easily identifies the diseases based on the
symptoms to predict various diseases.

• It is the frequent item set mining and association rule learning over relational
databases.

• It processed by identifying the frequent individual items in the database and

extending them to larger and larger item sets as long as those item sets appear
sufficiently often in the database.

3.3.1 EXISTING SYSTEM

 Existing data mining techniques with data mining algorithms and its application tools
which are more valuable for healthcare services.
 Existing data in different databases to transform it into new researches and results.
 It makes use of Artificial Intelligence, machine learning and database management to
extract new patterns from large data sets and the knowledge associated with these
patterns.

DISADVANTAGES

 A patient has to visit the doctor in person and still does not get proper treatment, as the
doctors are unable to predict the exact disease.
 Human error can be avoided with the help of computer assisted quality decision
making.
 This application requires active internet connection.
 User need to put correct data or else it behaves abnormally.

3.3.2 PROPOSED SYSTEM

 To overcome the drawback of existing system. Here, I have developed prediction

system.

22
  I have developed an expert system called Disease Prediction with the help of
symptoms using Data Mining, which is used for simplifying the task of doctors.
 A system checks a patient at initial level and suggests the possible diseases.
 It starts with asking about symptoms to the patient, if the system is able to identify the
appropriate disease then it suggests a doctor available to the patient in the nearest
possible area.
 If the system is not sure enough, it asks some queries to the patients, still if the system
is not sure then it will display some tests to the patient. Based on available cumulative
information, the system will display the result.
 This system not only simplifies task of the doctors but also helps the patients by
providing necessary help at an earliest stage possible.

ADVANTAGES

 This system helps to reduce the waiting time of the patient.

 Users can select the appointment time according to his preference.
 Available and booked slots are shown in effective Graphical User Interface.
 With the help of these designs, the system is designed and implemented which helps in
automation of the prediction system.

3.4 METHODOLOGY USED

3.4.1 MODULES

• Registration(Doctor/Patient)

• Login (Patients/Admin/Doctor)

• Admin

• Doctor

• Disease Prediction

• Appointments

23
 3.4.2 MODULES DESCRIPTION

1. REGISTRATION

This module is responsible for doctor and patient registration. They can use their user
Id and password through which they can login to the system. Doctor can register by giving
his/her name, address, contact number, email and his/her government registration number.
Patient can register by giving his/her name, address, contact number, email. The username
and password will be sent to the patient /doctor‘s registered email id.

Figure 3.1: Doctor’s Registration

24
 Figure 3.2: Patient’s Registration

2. LOGIN (ADMIN/DOCTOR/PATIENTS)

Admin, Doctor and Patients can login to the system using their ID and Password to
perform their work.

Figure 3.3: Admin Login

25
3. ADMIN PAGE
Admin is the owner of this system. He / She can login and do the following. Admin
can add disease details along with symptoms and type. Admin can view feedback provided by
various users.

Figure 3.4: Admin Page

4. DOCTOR PAGE

Doctor can register them and login to the system to view the appointments.

Figure 3.5: Doctor Page

26
5. PATIENT PAGE

Patient can login to the system Patient can search for doctor by specifying name,
address or type. They can book an appointment to the doctor.

Figure 3.6: Patient Page

6. DISEASE PREDICTION

Patient will specify the symptoms caused due to his illness. System will ask certain
question regarding his illness and system will predict the disease based on the symptoms
specified by the patient and system will also suggest doctors based on the disease.

27
 Figure 3.7: Disease Prediction

7. APPOINTMENTS

Doctor will get notification of appointments how many people had accessed the
system and what diseases are predicted by the system.

Figure 3.8: Appointments

28
 CHAPTER 4

4. RESULTS AND DISCUSSION

Here, I present the predicting results of my project by using Apriori algorithm here; I
have considered prediction of disease in dataset for discussion and this dataset with multiples
of attribute values and of 4 symptoms. A System was constructed and data was collected
from the users for analysis. The user should answer, the questions given in the template and
the system will predict whether the person have disease or not. Here, I have collected more
inputs from the user for prediction and these attributes are considered for analysis.

4.1 DISEASE PREDICTION RESULTS

Figure 4.1 Search Disease

In the above Figure 4.1(a), Here, I predict the disease using various symptoms by
using the data mining techniques. After that entering the symptoms, the system will
automatically predict what kind of disease will be occurred and then it will be generated.

29
 Figure 4.2 Predicted Diseases

In the Figure 4.1(b), The system will be predict the diseases and then it will shows the
available doctor in the nearest place and then users can book the appointments to consult the
doctor.

30
 CHAPTER 5
5. CONCULSION AND FUTURE ENHANCEMENT

5.1 CONCLUSION

As I have earlier mentioned, I solved the problem by predicting the disease by

considering the symptoms entered by the user. The main focus of my paper is to identify the
disease by calculating the numerical value based on the severity rating of the symptoms
and also make the prediction more accurate by considering the previous history of the user.
Although I have tested my system for viral and regular based diseases, it can be
extended to larger settings.

5.2 FUTURE ENHANCEMENT

The future work can be focus on by considering various types of diseases with large
number of symptoms in predicting more number of diseases. To improve the reliability of the
system the test results for various medical conditions will be helpful. Since the results are
dependent on the experience of previous users, it is important to isolate genuine experiences
from fake ones. We can predict the various diseases with the help of data mining techniques
and to analyze these diseases in a statistical method.

31
5.3 APPENDICES

5.3.1 CODING

ADMIN LOGIN

using System;

using System.Collections;

using System.Configuration;

using System.Data;

using System.Linq;

using System.Web;

using System.Web.Security;

using System.Web.UI;

using System.Web.UI.HtmlControls;

using System.Web.UI.WebControls;

using System.Web.UI.WebControls.WebParts;

using System.Xml.Linq;

namespace smarthealth

public partial class adminlogin : System.Web.UI.Page

protected void Page_Load(object sender, EventArgs e)

protected void Button1_Click(object sender, EventArgs e){

if (unam.Text == "admin" && pwd.Text == "admin")

32
{

Response.Redirect("adminpage.aspx");

else

DOCTOR LOGIN
using System;

using System.Collections;

using System.Configuration;

using System.Data;

using System.Linq;

using System.Web;

using System.Web.Security;

using System.Web.UI;

using System.Web.UI.HtmlControls;

using System.Web.UI.WebControls;

using System.Web.UI.WebControls.WebParts;

using System.Xml.Linq;

using System.Windows.Forms;

33
namespace smarthealth

public partial class doctorlogin : System.Web.UI.Page

connectioncls fn = new connectioncls();

protected void Page_Load(object sender, EventArgs e)

protected void TextBox1_TextChanged(object sender, EventArgs e)

protected void TextBox2_TextChanged(object sender, EventArgs e)

protected void Button1_Click(object sender, EventArgs e)

DataTable dt = new DataTable();

dt = fn.getid("select * from doctordetails where unam='" + TextBox1.Text + "' and pwd='" +

TextBox2.Text + "'");

if (dt.Rows.Count > 0)

Response.Redirect("doctorpage.aspx");

else

34
MessageBox.Show("Invalid USer");

protected void LinkButton1_Click(object sender, EventArgs e)

Response.Redirect("doctorregistration.aspx");

DOCTOR REGISTRATION
using System;

using System.Collections;

using System.Configuration;

using System.Data;

using System.Linq;

using System.Web;

using System.Web.Security;

using System.Web.UI;

using System.Web.UI.HtmlControls;

using System.Web.UI.WebControls;

using System.Web.UI.WebControls.WebParts;

using System.Xml.Linq;

using System.Windows.Forms;

namespace smarthealth
35
{

public partial class doctorregistration : System.Web.UI.Page

connectioncls fn = new connectioncls();

protected void Page_Load(object sender, EventArgs e)

specialization.Items.Add("Cardiology");

specialization.Items.Add("Neurology");

specialization.Items.Add("Ortho");

if (!IsPostBack)

DataTable de = new DataTable();

de = fn.getid("select district from districts");

if (de.Rows.Count > 0)

for (int i = 0; i < de.Rows.Count; i++)

district.Items.Add(de.Rows[i][0].ToString());

district.Items.Insert(0, new ListItem("Select District"));

if (!IsPostBack)

36
DataTable de = new DataTable();

de = fn.getid("select specialization from specialization order by specialization asc");

if (de.Rows.Count > 0)

for (int i = 0; i < de.Rows.Count; i++)

specialization.Items.Add(de.Rows[i][0].ToString());

specialization.Items.Insert(0, new ListItem("Select "));

protected void TextBox2_TextChanged(object sender, EventArgs e)

protected void TextBox3_TextChanged(object sender, EventArgs e)

protected void TextBox4_TextChanged(object sender, EventArgs e)

protected void DropDownList1_SelectedIndexChanged(object sender, EventArgs e)

37
protected void Button1_Click(object sender, EventArgs e)

DataTable de = new DataTable();

string status;

de = fn.getid("select regno from doctorreg where regno='"+ regno.Text +"' and doctornam='"+
doctonam.Text +"'");

if (de.Rows.Count > 0)

status = "yes";

else

status="no";

if(status=="yes")

/* de = fn.getid("select regno from doctordetails where regno='"+ regno.Text +"'");

if (de.Rows.Count > 0)

{*/

string un, pw;

un = email.Text;

pw = regno.Text + "123";

fn.connect();

fn.execute("insert into
doctordetails(regno,doctornam,district,addr,email,contact,specialization,unam,pwd) values('"

38
+ regno.Text + "','" + doctonam.Text + "','" + district.Text + "','" + addr.Text + "','" +
email.Text + "','" + contact.Text + "','" + specialization.Text + "','" + un + "','" + pw + "')");

MessageBox.Show("Registered Successfully");

MessageBox.Show("Username is" + un);

MessageBox.Show("Password is" + pw);

regno.Text = "";

doctonam.Text = "";

addr.Text = "";

contact.Text = "";

//specialization.Text = "";

email.Text = "";

/* }

else

MessageBox.Show("Registered already");

}*/

else

MessageBox.Show("Invalid register number");

protected void Button2_Click(object sender, EventArgs e)

DataTable de = new DataTable();

39
string status;

de = fn.getid("select * from doctorreg where regno='" + regno.Text + "'");

if (de.Rows.Count > 0)

doctonam.Text = de.Rows[0][1].ToString();

email.Text = de.Rows[0][2].ToString();

contact.Text = de.Rows[0][3].ToString();

VIEW DISEASE

using System;

using System.Collections;

using System.Configuration;

using System.Data;

using System.Linq;

using System.Web;

using System.Web.Security;

using System.Web.UI;

using System.Web.UI.HtmlControls;

using System.Web.UI.WebControls;

using System.Web.UI.WebControls.WebParts;

40
using System.Xml.Linq;

using System.Data.SqlClient;

using System.Windows.Forms;

namespace smarthealth

public partial class viewdisease : System.Web.UI.Page

string diseid;

connectioncls fn = new connectioncls();

protected void Page_Load(object sender, EventArgs e)

if (!IsPostBack)

this.BindGrid();

protected void OnPaging(object sender, GridViewPageEventArgs e)

GridView1.PageIndex = e.NewPageIndex;

GridView1.DataBind();

private void BindGrid()

string constr = ConfigurationManager.ConnectionStrings["sqlcon"].ConnectionString;

41
using (SqlConnection con = new SqlConnection(constr))

using (SqlCommand cmd = new SqlCommand("SELECT * FROM disease"))

using (SqlDataAdapter sda = new SqlDataAdapter())

cmd.Connection = con;

sda.SelectCommand = cmd;

using (DataTable dt = new DataTable())

sda.Fill(dt);

GridView1.DataSource = dt;

GridView1.DataBind();

protected void GridView1_SelectedIndexChanged(object sender, EventArgs e)

Session["disid"] = GridView1.SelectedRow.Cells[0].Text;

diseid = Session["disid"].ToString();

fn.connect();

fn.execute("delete from disease where diseaseid='" + diseid + "'");

MessageBox.Show("Record Deleted Successfully");

42
this.BindGrid();

PATIENT REGISTRATION

using System;

using System.Collections;

using System.Configuration;

using System.Data;

using System.Linq;

using System.Web;

using System.Web.Security;

using System.Web.UI;

using System.Web.UI.HtmlControls;

using System.Web.UI.WebControls;

using System.Web.UI.WebControls.WebParts;

using System.Xml.Linq;

using System.Windows.Forms;

namespace smarthealth

public partial class patientregistration : System.Web.UI.Page

connectioncls fn = new connectioncls();

43
string str, a;

DataTable de = new DataTable();

protected void Page_Load(object sender, EventArgs e)

district.Items.Add("Ramnad");

district.Items.Add("Madurai");

district.Items.Add("Sivagangai");

if (!IsPostBack)

de = fn.getid("select district from districts");

if (de.Rows.Count > 0)

for (int i = 0; i < de.Rows.Count; i++)

district.Items.Add(de.Rows[i][0].ToString());

district.Items.Insert(0, new ListItem("Select District"));

de = fn.getid("select max(patientid) from patientreg");

if (de.Rows.Count > 0)

for (int i = 0; i < de.Rows.Count; i++)

44
str = de.Rows[i][0].ToString();

if (str.Length > 0)

if (str.Length == 4)

a = str.Substring(3, 1);

else if (str.Length == 5)

a = str.Substring(4, 1);

else if (str.Length == 6)

a = str.Substring(5, 1);

patientid.Text = ((Convert.ToInt16(a)) + 1).ToString();

patientid.Text = "pat" +patientid.Text;

else

// MessageBox.Show("hi");

patientid.Text = "pat1";

45
}

protected void patientid_TextChanged(object sender, EventArgs e)

protected void TextBox2_TextChanged(object sender, EventArgs e)

protected void addr_TextChanged(object sender, EventArgs e)

protected void TextBox3_TextChanged(object sender, EventArgs e)

protected void TextBox4_TextChanged(object sender, EventArgs e)

protected void Button1_Click(object sender, EventArgs e)

string un, pw;

un = patientid.Text;

unam.ReadOnly = true;

fn.connect();

fn.execute("insert into
patientreg(patientid,nam,district,addr,email,contact,unam,pwd)values('" + patientid.Text +

46
"','" + patientnam.Text + "','" + district.Text + "','" + addr.Text + "','" + email.Text + "','" +
contact.Text + "','" + patientid.Text + "','" + pwd.Text + "')");

MessageBox.Show("Registered Successfully");

MessageBox.Show("Username is" + un);

MessageBox.Show("Password is" + pwd.Text);

Response.Redirect("patientlogin.aspx");

PREDICTED DISEASE AND DOCTOR

using System;

using System.Collections;

using System.Configuration;

using System.Data;

using System.Linq;

using System.Web;

using System.Web.Security;

using System.Web.UI;

using System.Web.UI.HtmlControls;

using System.Web.UI.WebControls;

using System.Web.UI.WebControls.WebParts;

using System.Xml.Linq;

using System.Data.SqlClient;

using System.Windows.Forms;

47
namespace smarthealth

public partial class diseaseanddoctor : System.Web.UI.Page

connectioncls fn = new connectioncls();

string regno, docnam, pnam, disease, distpat, contact, pttid;

string tp, distdoc,pid,spec;

protected void Page_Load(object sender, EventArgs e)

if (!IsPostBack)

TextBox3.Text = Session["disease"].ToString();

tp = Session["typ"].ToString();

distpat = Session["district"].ToString();

// MessageBox.Show(tp);

// MessageBox.Show(distpat);

pid = Session["appatid"].ToString();

Session["ptid"] = pid.ToString();

this.BindGrid();

private void BindGrid()

string constr = ConfigurationManager.ConnectionStrings["sqlcon"].ConnectionString;

48
using (SqlConnection con = new SqlConnection(constr))

using (SqlCommand cmd = new SqlCommand("SELECT * FROM doctordetails where

district='" + distpat + "' and specialization='"+ tp +"' "))

using (SqlDataAdapter sda = new SqlDataAdapter())

cmd.Connection = con;

sda.SelectCommand = cmd;

using (DataTable dt = new DataTable())

sda.Fill(dt);

GridView1.DataSource = dt;

GridView1.DataBind();

protected void OnPaging(object sender, GridViewPageEventArgs e)

GridView1.PageIndex = e.NewPageIndex;

GridView1.DataBind();

protected void TextBox3_TextChanged(object sender, EventArgs e)

49
{

protected void Button1_Click(object sender, EventArgs e)

protected void GridView1_SelectedIndexChanged(object sender, EventArgs e)

string status = "Booked";

pttid = Session["ptid"].ToString();

DataTable dt = new DataTable();

dt = fn.getid("select nam,district,contact from patientreg where patientid='" + pttid + "'");

if (dt.Rows.Count > 0)

pnam = dt.Rows[0][0].ToString();

distpat = dt.Rows[0][1].ToString();

contact = dt.Rows[0][2].ToString();

MessageBox.Show(pnam.ToString());

disease = TextBox3.Text;

regno = GridView1.SelectedRow.Cells[0].Text;

docnam = GridView1.SelectedRow.Cells[1].Text;

// diseid = Session["disid"].ToString();

distdoc=GridView1.SelectedRow.Cells[2].Text;

spec=GridView1.SelectedRow.Cells[4].Text;

50
MessageBox.Show(regno.ToString());

fn.connect();

MessageBox.Show(regno.ToString());

fn.execute("insert into
appointmentst(patientid,nam,disease,contact,docid,docnam,specialization,place,status)values('
" + pttid + "','" + pnam + "','" + TextBox3.Text + "','"+ contact +"','"+ regno +"','" + docnam +
"','"+ spec +"','"+ distdoc +"','"+ status +"' )");

MessageBox.Show("Appointment Booked Successfully");

GridView1.SelectedRow.Cells[5].Text = "Booked";

BOOK AN APPOINTMENT

using System;

using System.Collections;

using System.Configuration;

using System.Data;

using System.Linq;

using System.Web;

using System.Web.Security;

using System.Web.UI;

using System.Web.UI.HtmlControls;

using System.Web.UI.WebControls;

using System.Web.UI.WebControls.WebParts;

51
using System.Xml.Linq;

using System.Data.SqlClient;

using System.Windows.Forms;

namespace smarthealth

public partial class diseaseanddoctor : System.Web.UI.Page

connectioncls fn = new connectioncls();

string regno, docnam, pnam, disease, distpat, contact, pttid;

string tp, distdoc,pid,spec;

protected void Page_Load(object sender, EventArgs e)

if (!IsPostBack)

TextBox3.Text = Session["disease"].ToString();

tp = Session["typ"].ToString();

distpat = Session["district"].ToString();

// MessageBox.Show(tp);

// MessageBox.Show(distpat);

pid = Session["appatid"].ToString();

Session["ptid"] = pid.ToString();

this.BindGrid();

52
private void BindGrid()

string constr = ConfigurationManager.ConnectionStrings["sqlcon"].ConnectionString;

using (SqlConnection con = new SqlConnection(constr))

using (SqlCommand cmd = new SqlCommand("SELECT * FROM doctordetails where

district='" + distpat + "' and specialization='"+ tp +"' "))

using (SqlDataAdapter sda = new SqlDataAdapter())

cmd.Connection = con;

sda.SelectCommand = cmd;

using (DataTable dt = new DataTable())

sda.Fill(dt);

GridView1.DataSource = dt;

GridView1.DataBind();

protected void OnPaging(object sender, GridViewPageEventArgs e)

GridView1.PageIndex = e.NewPageIndex;

53
GridView1.DataBind();

protected void TextBox3_TextChanged(object sender, EventArgs e)

protected void Button1_Click(object sender, EventArgs e)

protected void GridView1_SelectedIndexChanged(object sender, EventArgs e)

string status = "Booked";

pttid = Session["ptid"].ToString();

DataTable dt = new DataTable();

dt = fn.getid("select nam,district,contact from patientreg where patientid='" + pttid + "'");

if (dt.Rows.Count > 0)

pnam = dt.Rows[0][0].ToString();

distpat = dt.Rows[0][1].ToString();

contact = dt.Rows[0][2].ToString();

MessageBox.Show(pnam.ToString());

disease = TextBox3.Text;

regno = GridView1.SelectedRow.Cells[0].Text;

docnam = GridView1.SelectedRow.Cells[1].Text;// diseid = Session["disid"].ToString();

54
distdoc=GridView1.SelectedRow.Cells[2].Text;

spec=GridView1.SelectedRow.Cells[4].Text;

MessageBox.Show(regno.ToString());

fn.connect();

MessageBox.Show(regno.ToString());

fn.execute("insert into
appointmentst(patientid,nam,disease,contact,docid,docnam,specialization,place,status)

values('" + pttid + "','" + pnam + "','" + TextBox3.Text + "','"+ contact +"','"+ regno +"','" +
docnam + "','"+ spec +"','"+ distdoc +"','"+ status +"' )");

MessageBox.Show("Appointment Booked Successfully");

GridView1.SelectedRow.Cells[5].Text = "Booked";

55
5.4 SCREEN SHOTS

LOGIN PAGE

ADMIN LOGIN

56
ADMIN PAGE

INDIVIDUAL ID OF DOCTOR’S REGISTRATION

57
ADD DISEASE

DOCTOR REGISTRATION

58
DOCTOR LOGIN

DOCTOR PAGE

59
PATIENT REGISTRATION

PATIENT LOGIN

60
PATIENT PAGE

SEARCH DISEASE

61
PREDICTED DISEASES

BOOKED APPOINTMENT

62
SEARCH DOCTOR

VIEW PATIENTS

63
VIEW APPOINTMENTS

64
5.5 REFERENCES

[1] Zoubida Alaoui Mdaghri, Mourad El Yadari, Abdelillah Benyoussef, Abdellah El Kenz
“Study and analysis of Data Mining for Healthcare”, 2016 4th IEEE International Colloquium
on Information Science and Technology.

[2] Prof. Krishna Kumar Tripathi, Shubham Jawadwar and Siddhesh Murudkar published
paper on “A Health Prediction Using Data Mining” in International Research Journal of
Engineering and Technology (IRJET),Volume: 05 Issue: 04 | Apr-2018.

[3] Jena, L., & Swain, R. (2017, December). Work-in-progress: Chronic disease risk
prediction using distributed machine learning classifiers. In 2017 International Conference
on Information Technology (ICIT) IEEE, 1(8), 170-173.

[4] “Survey on Data Mining Algorithms in Disease `Prediction”, by Kirubha V et al. IJCTT,
2016.

[5] Avinash Golande, Pavan Kumar T, “Heart Disease Prediction Using Effective Machine
Learning Techniques”, International Journal of Recent Technology and Engineering (IJRTE),
ISSN: 2277-3878, Volume-8, Issue-1S4, June 2019.

[6] Purushottam, Prof. (Dr.) Kanak Saxena, Richa Sharma “Efficient Heart Disease Prediction
System using Decision Tree”, 2015 IEEE International conference on computing,
communication and automation.

[7] Predicting Disease By Using Data Mining Based on Healthcare Information System” By
MIN CHEN, (Senior Member, IEEE), YIXUE HAO, KAI HWANG, ON HEALTHCARE
BIG DATA, 2017.

65
WEBSITES REFERRED

http://www.wikipedia.com/

http://www.w3schools.com/

http://www.youtube.com

https://github.com/topics/disease-prediction

https://www.geeksforgeeks.org/disease-prediction-using-machine-learning/

https://paperswithcode.com/task/disease-prediction

https://www.intel.com/content/www/us/en/developer/articles/reference-kit/disease-
prediction.html

66