DESIGN AND IMPLEMENTATION OF CANCER PREDICTION USING NAIVE BAYES

ABSTRACT

Cancer prediction is the identification of individual or clusters of predictive genetic alterations might help in defining the outcome of cancer treatment, allowing for the stratification of patients into distinct cohorts for selective therapeutic protocols. The aim of the study is to design and implement a Cancer Prediction using Naïve Bayes. In achieving this aim, the specific objectives were set out to design an system that allows the early detection of cancer, produce prompt and accurate result from test carried out in the hospital, and help in solving the problem of cancer detection. Investigation reveals the problems of the Cancer Prediction using Naïve Bayes: the presently practiced prediction at the hospital does not allow early detection of cancer, lack of immediate retrievals, lack of immediate information storage, and the preparation of accurate and prompt result from diagnosis: This becomes a difficult task since information needed may not be available as at when due. The programming language used is HTML, CSS, JAVASCRIPT, PHP, SQL and JQUERY. The reason why web programming languages was used is because, it is platform independent and it is a web based application. This research has a translational potential for patients, who have abnormal mammogram findings or who been diagnosed with cancer, it will also impact on the profession of sonography, because the development of new techniques could reduce the number of auxiliary dissections and makes the diagnostic process less invasive.

CHAPTER ONE

1.1 Introduction

As a prelude to other parts of this study, this chapter will discuss the background upon which this study was initiated, the statement of problems that led to this study, the Aim and Objectives of the study. Others are Significance of the study, Scope of work, Limitations of the Study and Definition of technical terms.

1.2 Background of Study

According to Wikipedia (2021), Naive Bayes is a simple technique for constructing classifiers: models that assign class labels to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set. There is not a single algorithm for training such classifiers, but a family of algorithms based on a common principle: all naive Bayes classifiers assume that the value of a particular feature is independent of the value of any other feature, given the class variable (Wikipedia, 2021).

Data classification process using knowledge obtained from known historical data has been one of the most intensively studied subjects in statistics, decision science and computer science. It has been applied in problems of medicine, social science management and engineering. Variable problems such as disease diagnosis, image recognition, and credit evaluation using classification techniques (Michie, Spiegelhalter, & Tayor, 1994). In medical and other domains, linear programming approaches were efficient and effective methods (Bennett & Mangasarian, 1992; Freed & Glover, 1981; Grinold, 1972; Smith, 1968). Recently, intelligent methods such as NN and support vector machines have been intensively used for classification tasks (Ryua, Chandrasekaranb, & Jacobs, 2007).One of the application areas of analyzing database and pattern recognition is automated diagnostic systems. The aims of these studies are to assist doctors in making diagnostic decision. These systems guide the user to collect easily the patient information, based on those information points that can lead to a possible diagnose and to the adapted treatment of the diseases. They guide the user during the medical examination (physical) that will be done on the patient showing the definitions, and explanation of the of the signs associated to their disease and verify that the doctor does not forget to examine none of the criteria diagnoses even though is the first time that he sees or knows this sign of the prostate or breast cancer.

Once the patient data collected, the diagnose is based on the stored medical knowledge. The data on symptoms or signs, special data of laboratory or tests or radiological images are process by the system using defined rules to obtain the possible diagnoses. Additional data such as the presence or absence of certain signs and symptoms of cancer help to make a final diagnose. The rules of these Expert systems for breast and prostate cancer include the diagnose criteria from world-wide Associations, as well as algorithms designed by doctors and members of the Laboratory of Federal Medical Center owerri. Therefore, in Nigeria where the research was carried out, the activities that was conducted is to know the Cancer Prediction using Naïve Bayes.

1.3 Statement of Problems

Investigation reveals the problems of the Cancer Prediction using Naïve Bayes:

The presently practiced prediction at the hospital does not allow early detection of cancer.
Lack of immediate retrievals: It is very difficult to retrieve patient’s information from the current system practiced in the hospital.
Lack of immediate information storage: The information generated by various transactions takes time and efforts to be stored at right place.
Preparation of accurate and prompt result from diagnosis: This becomes a difficult task since information needed may not be available as at when due.

1.4 Aim and Objectives of Study

The aim of the study is to design and implement a Cancer Prediction using Naïve Bayes. In achieving this aim, the specific objectives were set out as follows:

To design an system that allows the early detection of cancer.
To produce prompt and accurate result from test carried out in the hospital.
To help in solving the problem of cancer detection

1.5 Significance of Study

The following are the relevance of the Cancer Prediction using Naïve Bayes research work:

This research has a translational potential for patients, who have abnormal mammogram findings or who been diagnosed with cancer.
This topic will impact on the profession of sonography, because the development of new techniques could reduce the number of auxiliary dissections and makes the diagnostic process less invasive.
Finding new ways to determine the stage of metastatic cancer would have major clinical impact.
Clinical practice could change in screening auxiliary lymph nodes much like breast masses imaged without auxiliary dissection.
This method for auxiliary sonography may be of significant importance in the management of cancer patients.

Besides, the study will serve as reference material for subsequent researcher in the field or related topics.

1.6 Scope of Study

The study focuses on design and implementation on Cancer Prediction using Naïve Bayes technique. The research work will cover two cancer predictions which are:

Breast cancer, and
Prostrate cancer

The proposed system developed will only cover patient with symptoms for prediction / diagnosis and control solutions like drug control and supplement there will be room for diagnosing patient with various kind of symptoms with remedial solution and advice.

1.7 Limitations of the Study

During the course of this study, many things militated against its completion, some of which are:

Time Constraint: The time frame given to accomplish this project was very short due to school academic calendar and it was carried out under pressure which made the researcher not to implement some necessary features.
Establishment Policies: Establishment policies posed a serious limitation as most staffs are not ready to release information needed for this project work. There were lots of information needed from the staffs of this institution to enhance the study which took them time to release or they did not release at all for security purposes, hence the scope was reduced.
Research material: availability of research material is a major setback to the scope of the study.
Frequent power failure: This made the researcher append more money on fuel to ensure sustainable power.
Financial Constraint: Insufficient fund tends to impede the efficiency of the researcher in sourcing for the relevant materials, literature or information and in the process of data collection (internet, questionnaire and interview).

1.8 Definition of Terms

Information System: It is a collection of procedures, people, Instructions and equipment to produce information in a useful form.

Technology: It is study of techniques or process of mobilizing Resource (such as information) for accomplishing objectives that benefits man and his environment.

Information: Information can be defined as the process of gathering, transmitting, receiving, storing and retrieving data or several items put together to convey a desired message.

Databases: A systematically arranged collection of computer data, structured so that it can be automatically retrieved or manipulated. It is also called databank.

Virus: Is a small infectious agent that replicated only inside the living cells of other organisms.

Primate: Is any mammal of the group that includes the lemurs, lories, tarsiers, monkeys, apes and humans

Symptoms: Is the sign given when a particular thing want to happen

Vaccines: Are things you do to keep something away or to prevent a disease.

Diagnosis: The act or process of identifying or determining the nature and cause of a disease injury through evaluation, examination review of a patient laboratory data

PCR (polymerase chain reaction): is a biochemical technology in molecular biology use to amplify a single copy or a few copy of a piece of DNA across several orders of magnitude, generating thousands to millions of copies of particular DNA sequence.

…

CHAPTER TWO

2.0 Literature Review

2.1 Introduction

The review of relevant literature is the main topic of this chapter. A literature review covers theoretical and methodological contributions to a certain issue in addition to the state of knowledge at the time of writing. It provides information about the state of the art in relation to the subject you are writing about. It examines the body of work on the chosen subject. The literature evaluation in this study comprises the …

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TABLE OF CONTENTS
PRELIMINARY PAGES

Title page
Approval page
Dedication
Acknowledgement
Table of Contents
Abstract
CHAPTER ONE

INTRODUCTION

1.1 Introduction

1.2 Background of Study

1.3 Statement of Problems

1.4 Aim and Objectives of Study

1.5 Significance of Study

1.6 Scope of Study

1.7 Limitations of the study

1.8 Definition of Terms

CHAPTER TWO

LITERATURE REVIEW

2.1 Introduction

2.2 Expert Systems

2.3 History of Expert Systems

2.4 Advantages of Expert Systems

2.5 Disadvantages of Expert Systems

2.6 Prostate Cancer

2.7 Clinical Management Issues in Percutaneous Core Breast and Prostate Biopsy

2.8 Technical Issues and Pathologic Implications of Sentinel Lymph Node Biopsy in Early Stage Cancer Patients

2.9 Computer in Medicine

2.10 Applications of Experts System in Diagnosing Of Disease

2.10.1 Pathological Brain Detection (PBD)

2.10.2 Breast cancer

2.10.3 Lung cancer (bronchial carcinoma)

2.10.4 Colon cancer

2.10.5 Coronary Artery Disease

2.10.6 Congenital heart defect

2.10.7 Alzheimer's Disease

2.11 Data Classification Process in Breast Cancer

2.12 Theoretical Framework

CHAPTER THREE

SYSTEM ANALYSIS AND DESIGN

3.1 Methodology Adopted

3.1.1 Problem Identification Using SSADM

3.2 Analysis of the Existing System

3.2.1 Dataflow of the Existing System

3.2.2 Disadvantages Of The Existing System

3.2.3 Weakness of the existing System

3.3 Analysis of the Proposed System

3.3.1 Data Flow Diagram of the Proposed System

3.3.2 Advantages of the Proposed System

3.3.3 Justification of the Proposed System

3.4 Functional Requirements

3.4.1 Use Case Diagram Of The Admin / User Privileges

3.5 Data Requirements

3.6 High Level Model of the Proposed System

CHAPTER FOUR

SYSTEM DESIGN AND IMPLEMENTATION

4.1 Objectives of the Design

4.2 Cohesion and Decomposition High level Model

4.3 Control Center / Overall Dataflow Diagram

4.3.1 Proposed System Operation Flowchart

4.4 System Specification and Design

4.4.1 Input and Output Specification

4.4.2 Database Specification and Design

4.4.3 Data Dictionary

4.5 Choice and Justification of Programming Language

4.6 Program Documentation

4.7 Implementation Techniques

4.8 Programming Module Specification

4.8.1 Installation

4.9 Computer Hardware Minimum Requirement

4.10 Software Requirement

4.11 Personnel / User Training

CHAPTER FIVE

SUMMARY, CONCLUSION AND RECOMMENDATION

5.1 Introduction

5.2 Summary

5.3 Conclusion

5.4 Recommendation

REFERENCES

APPENDIX A - “SOURCE CODE”

APPENDIX B - “OBJECT PROGRAM”

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