BREAST CANCER PREDICTION SYSTEM

NOR FAZWANI BINTI DAGANG

BACHELOR OF COMPUTER SCIENCE

(SOFTWARE DEVELOPMENT)

UNIVERSITI SULTAN ZAINAL ABIDIN

2017

BREAST CANCER PREDICTION SYSTEM

NOR FAZWANI BINTI DAGANG

Bachelor of Computer Science (Software Development)

Faculty of Informatics and Computing

Universiti Sultan Zainal Abidin, Terengganu, Malaysia

MAY2017

i

DECLARATION

I hereby declare that this report is based on my original work except for quotations

and citations, which have been duly acknowledged. I also declare that it has not been

previously or concurrently submitted for any other degree at Universiti Sultan Zainal

Abidin or other institutions.

________________________________

Name : Nor Fazwani binti Dagang

Date : ..................................................

ii

CONFIRMATION

This is to confirm that:

The research conducted and the writing of this report was under my supervison.

________________________________

Name : PM Dr Fatma Susilawati binti Mohamad

Date : ..................................................

iii

DEDICATION

First of all thank you to ALLAH S.W.T for His mercy and guidance in giving

me the strength to complete this “Breast Cancer Prediction System” report on time.

Even facing with lots of difficulties in completing the task, I still manage to complete

it.

I would like to express my deepest sense of gratitude to my supervisor PM Dr

Fatma Susilawati Mohamad, who offered her continuous advice, idea, and

encouragement. Thank you for her effort and guidance in helping throughout this

semester.

Then I am thankful to my beloved parents for their love and continuous

support through thick and thin during my whole studies. Last but not least, I would

like to thank you to my classmates and friends who never give up on giving their

support and help in completing this task.

Thank you.

iv

ABSTRACT

Breast Cancer Prediction System is a web based system that has been prepared to be

use for user and it is an online prediction system. The problem that occurs is user or

patient is always having difficulties when they go to hospital to make a check up, but

the doctors were not available on that time. The purpose of this system is to allow user

to check whether they have breast cancer or not. User need to enter the details or

answer the questions that have been given and the cancer disease associated will

appear with those details. This system will give the prediction about breast cancer and

after that it will give the best advice and suggestion to the user. This system also

allows user to share their health related issues for breast cancer. It then processes user

specific details to check for various symptoms that could be associated with it. User

and admin also can create, update, delete and retrieve their profile. The added value of

this system is rule-based algorithm. Rule-based algorithm can be used for powering

prediction the disease.

v

ABSTRAK

Breast Cancer Prediction System adalah sistem berasaskan web yang disediakan

kepada pengguna dan ia adalah sistem ramalan atas talian. Masalah yang dihadapi

oleh pengguna atau pesakit ialah mereka selalu mengalami kesukaran apabila mereka

ke hospital untuk melakukan pemeriksaan, tetapi doktor tiada pada masa itu. Tujuan

sistem ini adalah untuk membenarkan pengguna untuk memeriksa sama ada mereka

mereka menghidap kanser payudara atau tidak. Pengguna perlu memasukkan

maklumat mereka atau menjawab soalan yang telah diberi dan maklumat yang

berkaitan dengan penyakit kanser akan muncul. Sistem ini akan memberi ramalan

tentang kanser payudara dan akan memberi nasihat dan cadangan yang terbaik

kepada pengguna. Sistem ini juga membenarkan pengguna berkongsi masalah

berkaitan kesihatan tentang kanser payudara. Ia akan memproses dan mengenalpasti

symptom yang berkaitan dengan masalah itu. Pengguna dan admin boleh create,

update, delete dan retrieve profil mereka. Nilai tambahan untuk sistem ini adalah

rule-based algoritma. Rule-based algoritma boleh digunakan untuk menjanakan

ramalan penyakit ini.

vi

CONTENTS

PAGE

DECLARATION i

CONFIRMATION ii

DEDICATION iii

ABSTRACT iv

ABSTRAK v

CONTENTS vi

LIST OF TABLES vii

LIST OF FIGURES xvi

LIST OF ABBREVIATIONS xv

CHAPTER I INTRODUCTION

1.1 Project Background 1

1.2 Problem statement 4

1.3 Objectives 4

1.4 Scopes 4

1.5 Expected Outcome 5

1.6 Limitation of Work 6

1.7 Project Planning 6

1.8 Report Organizing 6

1.9 Chapter Summary 7

CHAPTER II LITERATURE REVIEW

2.1 Introduction 8

2.2 Research Towards Existing System 9

2.3 Research Related with Others Method 10

2.4 Research on Related Techniques, Tools, and

Technologies

11

2.5 Rule-Based Concept Theory 12

2.6 Chapter Summary 14

vii

CHAPTER III

METHODOLOGY

3.1 Introduction 15

3.2 Justification Selection 15

3.3 Methodology Phases 17

3.3.1 Planning Phase 17

3.3.2 Risk Analysis Phase 17

3.3.3 Engineering Phase 17

3.3.4 Evaluation Phase 18

3.4 System Requirement 19

3.4.1 Hardware Requirement 19

3.4.2 Software Requirement 20

3.5 Framework 21

3.6 Context Diagram 23

3.7 Data Flow Diagram 25

3.7.1 DFD Level 1 Manage User Profile 27

3.7.2 DFD Level 1 Manage Questionnaire 28

3.7.3 DFD Level 1 Manage Result 29

3.7.4 DFD Level 1 Manage Admin profile 30

3.7.5 DFD Level 1 Manage Information 31

3.7.6 DFD Level 2 Manage User Profile 32

3.8 Entity Relationship Diagram (ERD) 33

3.9 Algorithms 34

3.10 Database Modelling 35

3.11 Chapter Summary 38

REFERENCES 39

viii

LIST OFTABLES

TABLE TITLE PAGE

3.1 List of Hardware 19

3.2 List of Software 20

ix

LIST OF FIGURES

FIGURE TITLE PAGE

3.1 Spiral Model 16

3.2 Framework 21

3.3 Context Diagram 23

3.4 Data Flow Diagram Level 0 25

3.5 DFD Level 1 for Manage User Profile 27

3.6 DFD Level 1 for Manage Questionnaire 28

3.7 DFD Level 1 for Manage Result 29

3.8 DFD Level 1 for Manage Admin Profile 30

3.9 DFD Level 1 for Manage Information 31

3.10 DFD Level 2 for Manage User Profile 32

3.11 Entity Relationship Diagram 33

3.12 Tables in Database for Breast Cancer System 35

3.13 Table Admin in Database 35

3.14 Table Information in Database 36

3.15 Table Questionnaire in Database 36

3.16 Table Result in Database 36

3.17 Table User in Database 37

x

LIST OF ABBREVIATIONS / TERMS / SYMBOLS

CD Context Diagram

DFD Data Flow Diagram

ERD Entity Relationship Diagram

FYP Final year project

xi

LIST OF APPENDICES

APPENDIX TITLE PAGE

A Gantt Chart FYP 1 41

1

CHAPTER I

INTRODUCTION

1.1 Project Background

Cancer or tumor is a group of disease that involve abnormal cell growth with

the potential to spread to other parts of the body but not all tumors are cancerous.

There are 100 types of cancer, including breast cancer, skin cancer, lung cancer,

colon cancer and lymphoma. Breast cancer is the one of the popular and second

leading cause of cancer death in women. The chance that a woman will die from

breast cancer is about 1 in 37 which is 2.7%.

Mostly the patient did not notice that they have breast cancer in the early stage

because breast cancer starts when cells in the breast begin to grow out of control.

This cell that from tumor usually can be seen on an x-ray or felt as a lump. Breast

cancer occurs almost in women, but men also can get breast cancer.

Each year, an estimated 1.6 milion new cases are diagnosed worlwide and in

2015, 560 thousand women die because of breast cancer (World Health

Organisation and National Cancer Registry of Malaysia 2005-2007). However,

death rates from breath cancer was dropped from 1989 to 2007. Since 2007, breast

cancer death rates have been steady in women younger than 50, but have

2

continued to decreased in older women (American Cancer Society’s Cancer

Statistic Centre,2016). The decreases of the death rates in older women is believed

that they noticed and get result of finding breast cancer earlier through screening

and increased awareness which is get the treatment as soon as possible.

Breast cancer is hard to diagnose but when finding the breast cancer as early as

possible, it will gives a better chance of successful treatment. To found out the

breast cancer, it need to do the screening test. Screening test can help find breast

cancer in its early stages, even before any symptoms appear. There are some

common symptom of breast cancer. The most common symptom is a new lump or

mass. A painless, hard mass that has irregular edges is more likely to be cancer,

but breast cancers can be tender, soft, or rounded. So that, if there have any new

breast mass or lump or breast change, it is important to checked by a health care

provider experienced in diagnosing breast disease.

So that, it is important to all people especially women to be aware of changes

in the breasts and to know the signs and symptoms of breast cancer. In this project,

we propose a rule-based algorithm that functions as a reliable decision support

system for breast cancer prediction.

3

Next, this project will arranged as starting by Chapter 1 that describes on the

introduction of the project, followed by Chapter 2 that explained on the literature

review that related to the previous research. After that, Chapter 3, the project

methodology, Chapter 4 deals with project design and modelling and lastly

Chapter 5 that concludes and identify the results of the project. The main goal of

this research is to develop a system that can be used by a person for predict

whether the person have breast cancer or not based on the symptoms details and

suggest to the patients what they need to do after that.

4

1.2 Problem Statement

The problem for this project is cancer are difficult to diagnose at early stages until

it comes to stage III and IV. Some people have difficulties to go to hospital to

make check up regularly because they lack of times or doctors did not free on that

time. Some people maybe have different symptoms and it will not be easily to

assume whether they have breast cancer or not. The possible symptoms of breast

cancer are swelling of all or part of a breast, skin irritation or dimpling, breast or

nipple pain, nipple retraction, redness, scaliness or thickening of nipple or breast

skin and nipple discharge that other than breast milk.

1.3 Objectives

i. To design and develop Breast Cancer Prediction system which can

help user to predict early sign of breast cancer.

ii. To apply Rule-Based algorithm for detecting symptoms of breast

cancer.

iii. To test and evaluate the proposed systems with the real cases.

1.4 Scope

This system will focus on Registered User, Admin and System Scope.

i. Registered User

The user that want to check or predict the health status on breast cancer

based on the symptoms that they have.

The user need to register/sign up to be a member and then login to

access the system.

Do prediction of breast cancer using the system.

ii. Admin

5

The person who will coordinate this system and update the system

based on situation.

iii. System

Login Module

- There is a registration and login access for user and admin to access

this system.

Evaluation Module

- Registered User will answer and evaluate the questionaires based

on what this system provide to find out the result.

Advice and Suggestion Module

- The system will give advices and suggestions to the user after they

predict the cancer and the result is positive.

Domain System (cancer)

- The result will generate based on the answer from Registered User

and analyze it with Rule-Based algorithm.

1.5 Expected Outcome

This system is expected to give an accurate prediction about the breast cancer

based on the symptoms that user have enter the details or answered the questions

that have be given. This system also expected to give advices and suggestions to

user after they predict the cancer and allow the user to share their health related

issues for breast cancer.

6

1.6 Limitation of Work

This system will focuses on prediction of breast cancer based on the details

that have be given. This system does not give the decision on what the stages of

cancer ( I,II,III or IV), it just only to predict whether the person have breast cancer

or not. The result may not be 100% accurate but the result from this system will

help the user to quickly take action and alert for the breast cancer by seek

consultant from doctor or diagnose the breast cancer using screening test.

1.7 Project Planning

Detailed project planning that has been implemented to facilitate system

development can referred to Appendix A.

1.8 Report Organizing

The reports organizing each chapter that have on the reort it arrange will refer

to the specific format and it easy for readers to understand the whole of the report.

The report is started with chapter 1 that explains about introduction, problem

statement, objective, scope, limitation of work, expected outcome and project

planning. The next chapter 2 explains about the literature review related paper

research for the system development. Then, chapter 3 discussed about project

methodology and requirement of software and hardware that guide the system

development. Chapter 4 deals with project design and modelling are the core part

in the development process. Lastly, chapter 4 conclude on the project

development.

7

1.9 Chapter Summary

In this chapter it will deliver about the early stages about this project

development. It explains more about initially project development process.

8

CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

Basically, in this chapter, the study of previous research is done. The related

journal and articles was analyzed to find out what are the weakness of the previous

research that we can overcome. Research paper related to rule-based has proved to be

a powerful tool for decision making system, such as expert systems and pattern

classification systems. Rule-based has already been used in some medical expert

systems. The related system of cancer prediction is also been reviewed to help in

understanding and gain knowledge about how to implement the system in the real

applications. Here, some of the previous paper that has some weakness that can be

solved through this project.

9

2.2 Research towards Existing System

Breast cancer is one of cancer killer in the world and can also kill men as well,

not only women. Early detection of cancer is essential for a fast response and better

chances of cure. But it is difficult to detect when it is in beginning because some of

the symptoms of the disease are absent at the beginning. Machine learning methods

and clinical factors was use to develop tools of cancer management. Rule based is one

of the machine learning methods. It has proved to be a powerful tool of decision

making systems. Rule based set theory has already been used in some medical expert

system.

In traditional rule-based approach, knowledge is encoded in the form of

antecedent consequent structure. When new data is encountered, it is matched to the

antecedents clauses of each rule, and those rules where antecedent match a data

exactly are fired, establishing the consequent clauses. This process continues until

desire conclusion is reached. In the past decade, fuzzy logic has proved to be

wonderful tool for intelligent systems in medicine. Some examples of using fuzzy

logic to develop fuzzy intelligence systems are fuzzy systems in their micro

processors, fuzzy cameras and camcorders that map image data to lens settings, and

fuzzy voice commands “up”, “land”, “hover” to control unmanned helicopters. (Bart

Kosko, Fuzzy Engineering, Prentice Hakk, 1997).

10

2.3 Research Related with Others Method

Breast Cancer Diagnosis by using k-Nearest Neigbor with Different Distance

and Classification Rules by Seyyid Ahmad Medjahed, Tamazouzt Ait Saadi,

Abdelkader Benyettou, 2013. This paper is to analyze the distance by using different

values of the parameters “k” and by using several rules of classificarion and to

evaluate the performance that can be used in the K-NN algorithms. In this paper, they

study and analyze several distance and different values of the nearest neighbors

parameter k, by using different classification rules in the k-nearest neighbor algorithm.

K-nearest neigbors algorithms is one of the most used algorithms in machine learning

and its a learning method based on instances that does required a learning phase.

Before classifiying anew element, they compare it to other elements using a similarity

measures. Its k-nearest neigbors are then considered, the class that appears most

among the neighbors is assigned to the element to be classified. The neighbors are

weighted by the distance that separate it to the new elements to classify.

Next, Tumor-Infiltrating CD8+ Lymphocytes Predict Clinical Outcome in

Breast Cancer by Sahar M.Amahmoud, Emma Claire Paish, Desmond G. Powe, 2011

has applied CD8+ T-Cell Quantification Method in this paper. Based on this paper, the

number of CD8+ Tlymphocytes was counted in each tumor core by using Nikon

Eclipse 80i micrscope and an eyepiece graticule by an investigator who had no

previous kniwledge of the patients clinical background. CD8+ T-Cellwere counted in

three locations in each tumor. The total number of CD8+ T-Cell was determinrd by

combining the counts for the three compartments and the scores were randomly re-

examined by the same investigator after a period of time to ensure reproducibility.

11

2.4 Research on Related Techniques, Tools, and Technologies

Several techniques had been defined by experts such as CD8+ Tlymphocytes

Cell Quantification, k-Nearest Neigbor and some machine learning applications.

Machine learning relates the problem of learning from data samples to the general

concept of inference. Based on the paper by Konstantina Kourou, Themis P.Exarchos,

Konstantinos P. Exarchos, Michalis V. Karamouzis, Dimitrios I. Fotiadis in Machine

Learning applications in cancer prognosis and prediction, 2014, they present a review

of recent Machine Learning approaches employed in the modelling of cancer

progression. The predictive models discussed are based on various supervised

Machine Learning technique as well as on different input features and data samples.

Then, they present the most recent publications that employ these techniques.

Another paper that using Data Mining Techniques is Diagnosis of Lung

Cancer Prediction System Using Data Mining Classification Techniques by V.

Krishnaiah, Dr. G. Narsimha, Dr. N. Subhash Chandra, 2013. This paper is to examine

the potential use of classification based data mining techniques, propose a model for

early detection and correct diagnosis of the disease and to summarize various review

and technical articles on diagnosis of lung cancer. In this study, they briefly examine

the potential use of classification based data mining techniques such as Naive Bayes,

Rule-Based, Decision Tree and Neural Network to massive volume of healthcare data.

They list the various analysis tasks that can be goals of discovery process and lists

methods and research areas and then compare the result.

12

2.5 Rule-Based Concept and Theory

A rule-based system is a set of “if-then” statements that uses a set of

assertions, to which rules on how to act upon those assertions are created. In software

development, rule-based systems can be used to create software that will provide an

answer to a problem in place of a human expert. This type of system may also be

called an expert system. Rule-based systems are also used in AI (Artificial

Intelligence) programming and systems.

Example of Rule-Based Concept:

IF (fever (high)) THEN Malaria

IF (Coldness) THEN Malaria

IF (Throb) THEN Malaria

IF (Sweat) THEN Malaria

IF (Sometimes colour of urine is black water fever) THEN Malaria

IF (Headache) THEN Malaria

IF (Vomiting) THEN Malaria

IF (Muscle pain) THEN Malaria

IF (High temperature) THEN Malaria

IF (Diarrhoea) THEN Malaria

IF (Coma (Seizure)) THEN Tuberculosis

IF ( Stiff Neck) THEN Tuberculosis

IF (Headache) THEN Tuberculosis

IF (AbdoIfminal Pain) THEN Tuberculosis

IF ( Weight Pain) THEN Tuberculosis

IF ( Fever) THEN Tuberculosis

13

IF (Masses along the neck) THEN Tuberculosis

IF (Draining Sinus) THEN Tuberculosis

IF (Small Reddish brown lesions ( face, eyelid, nose, cheek and ear)) THEN

Tuberculosis

IF (Reddish brown wart-like growth on the body) THEN Tuberculosis

IF (Skin lesions on hand, feet, elbow and knees) THEN tuberculosis.

IF ( Ulcer or abscesses on the Skin) THEN Tuberculosis

IF ( Necrosis of infected Skin) THEN Tuberculosis

IF (Stiffness of affected area) THEN Tuberculosis

IF (Blood present in Urine) THEN Tuberculosis

IF (Painful or uncomfortable Urination) THEN Tuberculosis

IF ( Hemopysis (coughing up blood)) THEN Tuberculosis

14

2.6 Chapter Summary

This chapter discussed about the collection of literature review that had been

reviewed during the feasibility studies. The literature review helps developer to

discover the problem of previous research or systems which needs to be overcome in

this system development. Besides that, it also can gain understanding about the system

that undergo the development process.

15

CHAPTER 3

RESEARCH METHODOLOGY

3.1 Introduction

This chapter explains details of methodology being used in software

development. The project methodology is important in every project because it helps

to organize investigation in a scientific ways to overcome problems, hunting for facts

or truth about the subject in order to achieve the objectives of project. In order to have

a good project, it should begin with a good understanding on user’s needs.

3.2 Justification Selection

The methodology for the system development that had been used extensively

is Spiral Model. Based on the Spiral Development Model that developed by Barry

Boehm (Boehm, 1998) had a profound impact on life cycle modelling and process

architecture. It means that the Spiral Model is the most appropriate used for

development of some system.

The Spiral Model which clearly divides the phases into four which are

Planning, Risk Analysis, Engineering and Evaluation. For every phase, some activity

has been allocated. The figure below describes briefly what activities involve for each

of the phases. Thus, developer will have clear understanding about what to do for

every phases. The allocation of activities for each phase consequence in the cost and

resource estimation thus to help in reducing risk during development.

16

Figure 3.1 Spiral Model

17

3.3 Methodology Phases

3.3.1 Planning Phase

In this phase, planning phase discussed a reason for selected goals include the

detail overview of the goals. In this phase, first objective of the project are describe to

identify a risk for fit people who can possibly get cancer and to make early detection.

The title of this project has selected, Breast Cancer Prediction System. The abstract

was done with all information gathered. Then, the entire requirement that involved in

the system will be identified.

3.3.2 Risk Analysis Phase

In risk analysis phase, the requirements are studied and brain storming sessions

were done to identify the potential risks. The risk that may exist is when it is difficult

to differentiate the symptoms or it may be risk when the information of the symptoms

is false. Once the risk was identified, risk mitigation strategy is planned and finalized.

3.3.3 Engineering Phase

This phase involve the actual development and testing. Breast Cancer

Prediction System will be developed and will be tested. It will combine all the

modules to become a complete system and will do integrating testing to make sure this

system will function nicely. The development involves code, test cases, results, test

summary, and report.

18

3.3.4 Evaluation Phase

In evaluation phase, admin will use and evaluate of the system. Then, user will

provide their feedback and approval for the system. The features implemented

document will be an output from this phase.

19

3.4 System Requirement

System requirement is the pre-requisites for a system to be run on a device.

System requirement are often used as opposed to an absolute rule. There are two

requirements that need to be considered in the development process, software and

hardware requirement.

3.4.1 Hardware Requirement

The list of software that used to develop this system is shown in table below:

Table 3.1 List of Hardware

HARDWARE DESCRIPTION

Laptop HP

Processor: AMD A8-6410 APU with AMD Radeon R5 Graphic 2.00

GHz

RAM: 4.00 GB

OS: Windows 8.1

Printer HP Deskjet 3630 series

20

3.4.2 Software Requirement

The list of software that used to develop this system is shown in table below:

Table 3.2 List of Software

SOFTWARE DESCRIPTION

Microsoft Office Word 2007 As platform for documentation and presentation

Edraw Max Tool to draw diagrams (CD, DFD, framework and interfaces)

Mozilla Firefox, Google

Chrome

Browser for running a system and find research and information

about the system.

XAMPP version 3.2.2 Act as a local server to run and test the system.

MySQL Database

Open source relational database management system that uses

structured Query Language and store the data of the system.

Dropbox Application for backup the system and data.

21

3.5 System Framework

Figure 3.2 shows the framework for Breast Cancer Prediction System.

Figure 3.2 Framework

22

Description of framework:

Based on the figure 3.2, it shows the framework on how the system running.

Firstly, user need to register and then login to access the Breast Cancer Prediction

System. All the data of user that had been register will save into database. Then, user

needs to answer the questionnaires given to diagnose the cancer and then system will

generate the results. After that, system will give suggestions and preventions for the

disease after user get their result. The suggestions and preventions are based on the

result of diagnose the user based on the result answers of questionnaires given. Lastly,

user can see results and recommendation given by the system. All of the steps are

saving into the database. Next, admin can login into Breast Cancer Prediction System.

Admin can look up the results of the user and admin also can update the

questionnaires.

23

3.6 Context Diagram

Context diagram explains the flow of the system based on the entities

and main process that involve in the system functional. It just describes the

main function of the system.

Figure 3.3 Context Diagram

24

Description:

The context diagram for Breast Cancer Prediction System is shown in the

figure above. THE BREAST CANCER PREDICTION process is at the centre of the

diagram. There are two entities in this system which is USER and ADMIN. The

entities are placed around the central process. Ten data flows are involved in the

interaction between the central process and the entities. The USER entity has three

incoming data flows which are Cancer Evaluation, Breast Cancer Information and

Prediction Result. USER also has three outgoing data flows, Register/Login, Personal

Details and Answer Questionnaires. The ADMIN entity has only one incoming data

flow which is User Details and has three outgoing data flows. There are Update

Information, Login and Update Questionnaires.

25

3.7 Data Flow Diagram Level 0

Data flow diagram shows the flow of the data that through in this system. That

shows the data will save in the database with specific table that have been created in the

database.

Figure 3.4 Data Flow Diagram Level 0

26

Description of DFD Level 0:

The DFD has two entities which are User and Admin. Manage User Profile,

Manage Questionnaire, Manage Result, Manage Suggestion, Manage Admin Profile,

Manage Information and Report are the seven process involve in the system. There are

four data stores created in the system which are User, Questionnaire, Admin and

Information.

1. User enter the details which are email, username, password, first name,

last name, gender,status and state to register and log in process that is

user profile process which outputs the details into user data store.

2. A user inputs the answer details of the questionnaire into questionnare

process which output answer details into questionnaire data store.

3. The output from the questionnaire data store which is result details will

input to the result process and output to the user.

4. The output of the questionnaire data store which is suggestion details

will input to the suggestion process and send to user.

5. Admin input the update questionnaire into questionnaire process and

output to questionnaire data store.

6. Admin input the admin details into admin profile process to admin data

store.

7. Admin input the information details about breast cancer into

information process which output information details to information

data store.

8. All the entities will input the report to report process and all the data

store will input the report to report data store.

27

3.7.1 Data Flow Diagram Level 1

Manage User Profile

Figure 3.5 Data Flow Diagram Level 1 for Manage User

Description:

1. User input user details to Register process and output user details to User

data store.

2. The user details from User data store are input to Update Details process

and User input user details to Update Details process and output Updated

User Details to User data store.

28

3.7.2 Data Flow Diagram Level 1

Manage Questionnaire

Figure 3.6 Data Flow Diagram Level 1 for Manage Questionnaire

Description:

1. Admin input Questionnaire Details to Add Questionnaire process and output

Questionnaire Details to Questionnaire data store.

2. The questionnaire details from Questionnaire data store are input to Update

Questionnaire process and admin input questionnaire details to Update

Questionnaire process and output Updated Questionnaire Details to

Questionnaire data store.

3. Admin input Questionnaire Details to Delete Questionnaire process and output

questionnaire details to Questionnaire data store.

29

3.7.3 Data Flow Diagram Level 1

Manage Result

Figure 3.7 Data Flow Diagram Level 1 for Manage Result

Description:

1. User input answer details to Answer Questionnaire process and output answer

detail Questionnaire data store.

2. Questionnaire answer from Questionnaire data store input to Result process

and output Result of Prediction to User.

30

3.7.4 Data Flow Diagram Level 1

Manage Admin Profile

Figure 3.8 Data Flow Diagram Level 1 for Manage Admin Profile

Description:

1. Admin input admin details to Add Admin process and output admin details to

Admin data store.

2. The admin details from Admin data store are input to Update Admin process

and Admin input admin details to Update Admin process and output Updated

Admin Details to Admin data store.

3. Admin input Admin Details to Delete Admin process and output admin details

to Admin data store.

31

3.7.5 Data Flow Diagram Level 1

Manage Information

Figure 3.9 Data Flow Diagram Level 1 for Manage Information

Description:

1. Admin input information details to Add Information process and output

information details to Information data store.

2. The information details from Information data store are input to Update

Information process and Admin input information details to Update

Information process and output Updated Information Details to

Information data store.

3. Admin input Information details to Delete Information and output the information

details to Information data store.

32

3.7.6 Data Flow Diagram Level 2

Manage User Profile

Figure 3.10 Data Flow Diagram Level 2 for Manage User Profile

Description:

1. A User Update Password in the Update Password process by sending New

Password at User data store.

2. A User send new data to Update Email at Update Email process by sending

New Email to User data store.

33

3.8 Entity Relationship Diagram (ERD)

Entity Relationship Diagram (ERD) is a data model which is tools used in

analysis to describe the data requirement and assumptions in the system from top-down

perspectives. The ERD shows it using derived table. Derived are used for make a

relationship between two or more main tables, it will have only record of foreign key

from the main table.

Figure 3.11 Entity Relationship Diagram (ERD)

34

3.9 Algorithms

1. Start

2. Answer twenty questions about self information and the risk of breast cancer

3. Tick the answers

4. Each answer have its value which are 0 for no and 1 for yes

5. Select value1 and value2 from radio button

6. Value = value1 + value2

7. If ($Value ≤ 8) {

8. Display message "You are at LOWER risk of Breast Cancer !";

9. }

10. else if (9 ≤ $Value ≤18){

11. Display message "You are at INTERMEDIATE risk of Breast

Cancer!”;

12. }

13. else

14. Display message "You are at HIGH risk of Breast Cancer !”;

15. }

16. End

35

3.10 Database Modelling

There are five tables available in the database which is Admin, Information,

Questionnaire, Result, and User. Each table have their attributes in the column.

Figure 3.12 Tables in Database for Breast Cancer System

3.10.1 Table Admin

Figure 3.13 Table Admin in Database

Table Admin contain adminID, adminName, adminPass, adminEmail, and phone. In

this table, adminID is the Primary Key.

36

3.10.2 Table Information

Figure 3.14 Table Information in Database

In this table, it contains infoID, infoDetail and date. The infoID is the Primary Key.

3.10.3 Table Questionnaire

Figure 3.15 Table Questionnaire in Database

Table questionnaire contains questionID and question and the Primary Key is

questionID.

3.10.4 Table Result

Figure 3.16 Table Result in Database

This table contains resultID, suggestion and user_score which is resulted is the

Primary Key.

37

3.10.5 Table User

Figure 3.17 Table User in Database

Table user contains username, password, email, name, gender and status and the

Primary Key is username.

38

3.11 Chapter Summary

This chapter discusses methodology for the system development, hardware and

software required in order to develop the system thus make them able to run on

specific platform. Every phase in development follows the project methodology that

mention in this chapter. System requirement which is hardware and software required

for developing system is briefly explained. Throughout this chapter also focussed

about data modelling which are context diagram, data flow diagram and entity

relationship diagram. In data modelling, the discussion is more about the structure of

the data represent in the database. Diagrams were constructed in order to give more

understanding of the system.

39

REFERENCES

1. World Health Organisation and National Cancer Registry of Malaysia 2005-

2007.

2. American Cancer Society’s Cancer Statistic Centre, 2016.

3. Bart Kosko, Fuzzy Engineering, Prentice Hakk, 1997.

4. V. Krishnaiah, Dr. G. Narsimha, Dr. N. Subhash Chandra, 2013. Diagnosis of

Lung Cancer Prediction System Using Data Mining Classification Techniques.

5. Konstantina Kourou, Themis P.Exarchos, Konstantinos P. Exarchos, Michalis

V. Karamouzis, Dimitrios I. Fotiadis, 2014. Machine Learning Applications in

Cancer Prognosis and Prediction.

6. Sahar M.Amahmoud, Emma Claire Paish, Desmond G. Powe, 2011. Tumor-

Infiltrating CD8+ Lymphocytes Predict Clinical Outcome in Breast Cancer.

7. Seyyid Ahmad Medjahed, Tamazouzt Ait Saadi, Abdelkader Benyettou, 2013.

Breast Cancer Diagnosis by using k-Nearest Neigbor with Different Distance

and Classification Rules.

8. A. Priyanga, S. Prakasam, 2013. Effectiveness of Data Mining-based Cancer

Prediction System (DMBCPS).

9. Kyu-Won Jung, Sohee Park, Young-Joo Won, Hyun-Joo Kong, Joo Young

Lee, Hong Gwan Seo, Jin-Soo Lee, 2012. Prediction of Cancer Incidence and

Mortality in Korea.

10. Adewole K. S., Hambali M. A., Jimoh M. K., 2015. Rule-Based Expert

System for Disease Diagnosis.

40

11. Data Mining - Rule Based Classification. 2017.

https://www.tutorialspoint.com/data_mining/dm_rbc.htm. Accessed on 12

February 2017.

12. Jermal A,Murray T,Samuels A,Ghafoor A,Ward E,Thun MJ,2003. Cancer

Journal for Clinician.

41

APPENDIX A: GANTT CHART FYP1