ABSTRACT
This research work focused on the development of fuzzy logic algorithm for diagnosis of malaria, a case study of General hospital Ikot Ekpene. A fuzzy logic system is an artificial intelligence based system that involves drawing conclusion from a partial truth. In the diagnosis of malaria, there are symptoms that the patients are tested for, when these symptoms match the known symptoms for malaria, conclusion is drawn that the patient has malaria. The problems that necessitated the development of the system include: blood testing for diagnosis of malaria is time consuming and expensive, health care facility should be accessible by all at all times, medical expert maybe under pressure. The objectives of the study included: to create a system that will help in diagnosis of malaria using fuzzy logic, to create a system to help simplify the task of a Doctor in diagnosing malaria, to create a system to improved efficiency by systematically keeping patient diagnosis records and timely generation. The software development methodology used is object oriented analysis and design methodology (OOADM). The programming language used is Visual BASIC and the database used is Microsoft Access 2003.
TABLE OF CONTENTS
Title Page – – – – – – – – i
Certification – – – – – – – – – ii
Approval Page – – – – – – – – iii
Dedication – – – – – – – – iv
Acknowledgment – – – – – – – – v
Abstract – – – – – – – – vi
Table of Contents – – – – – – – – vii-ix
List of Tables – – – – – – – x
List of Figures – – – – – – – – xi
CHAPTER ONE: INTRODUCTION
1.0 Introduction – – – – – – – 1-2
1.1 Background of the Study – – – – – – 2
1.2 Statement of Problem – – – – – 3
1.3 Aim and Objectives of the Study- – – – – 3-4
1.4 Scope of the Study – – – – – – – 4
1.5 Significance of the Study – – – – – – 4
1.6 Organization of Research – – – – – – 4-5
1.7 Definition of Terms – – – – – – 6
CHAPTER TWO: LITERATURE REVIEW
- Introduction – – – – – – – – 7
2.1 Theoretical background – – – – – – 7-8
- The Concept of Fuzzy Logic – – – – –
9-11
- Fuzzy Logic in Medical Diagnosis – – – – 11-13
- Applied Fuzzy Methods in Disease Diagnosis – – – 13-14
- Fuzzy set theory and fuzzy memberships – – – 15
- Fuzzy Logic in Medical Decision Support Systems – – 16-17
- Fuzzy Logic in Malaria Diagnosis – – – 17-19
CHAPTER THREE: SYSTEM ANALYSIS AND DESIGN
3.0 Introduction – – – – – – – 20
3.1 Research Methodology – – – – 20
3.2 Analysis of the Existing System – – – – 21
3.2.1 Advantages of the Existing System – – 21
3.2.2 Disadvantages of the Existing System – – 22
3.3 Analysis of the Proposed System – – – 22-23
3.3.1 Advantages of the Proposed System – – 24
3.4 System Design – – – – – – 24
3.4.1 Input Layout – – – – – 24-25
3.4.2 Output Layout – – – – – 24
3.4.3 Algorithm – – – – – – 25-26
3.4.4 Program Flowchart – – – – 27-31
3.4.5 Database Design – – – – – 32
3.4.6 System Architecture – – – – 33
3.3.7 Use Case/Class Diagram- – – – – 33
CHAPTER FOUR: SYSTEM IMPLEMENTATION AND DOCUMENTATION
4.0 Introduction – – – – – – 35
4.1 System Design Diagram – – – – – 35
4.2 Choice of Programming Language – – 36
4.3 Analysis of Modules – – – – 36
4.4 Programming Environment – – – – 37
4.4.1 Hardware Requirements – – – – 37
4.4.2 Software Requirements – – – 37
4.5 Implementation – – – – – – 37-38
CHAPTER FIVE: SUMMARY, RECOMMENDATIONS AND CONCLUSION
5.0 Introduction – – – – – – – 39
5.1 Constraints of the Study – – – – – – 39
5.2 Summary – – – – – – 39-40
5.3 Conclusion – – – – – – 40
5.4 Recommendations – – – – – – 41
References – – – – – – 42-44
Appendix A (Source Code) – – – – – 45-49
Appendix B (Output) – – – – – – 50-55
LIST OF TABLES
Table 3.2: Patient RegistrationDatabase Design – – 32
Table 3.3: Fuzzy logic diagnosis database design – – 33
LIST OF FIGURES
Figure 2.1: Patient registration form – – – – 8
Figure 3.1 : Patient registration input layout – – – 24
Figure 3.2: Fuzzy logic diagnosis layout – – – 25
Figure. 3.3 Login flowchart – – – – – 27
Figure. 3.4 Main Menu Flowchart – – – – 28
Figure 3.5 Patient Registration Flowchart – – – 29
Figure 3.6: Diagnosis flowchart – – – – – 30
Figure 3.7: Existing records – – – – – 31
Figure 3.8: Architecture of the System – – – – 33
Figure 3.9: Use case Diagram – – – – – 34
Figure 4.1: System Design Diagram – – – – 35
CHAPTER ONE
INTRODUCTION
- Introduction
This research presents a novel method for malaria diagnosis which is Fuzzy Logic. In this method, based on the selection of the problem, the expert system gives some symptom from which the user needs to select from. Based on the selection of symptoms, the user is again asked some questions. According to the answer selected, the fuzzy expert system diagnoses the disease based on it’s knowledge, add catalyst factor (if any), do ranking and gives the result in fuzzy form. As fuzzy expert system deals with uncertainty and vague terms, it is generally accepted in different spheres of life [1].
Fuzzy logic is an expert system algorithm used for solving problems. Fuzzy logic can be conceptualized as a generalization of classical logic. Modern fuzzy logic was developed by Lotfi Zadeh in the mid-1960s to model those problems in which imprecise data must be used or in which the rules of inference are formulated in a very general way making use of diffuse categories. In fuzzy logic, which is also sometimes called diffuse logic, there are not just two alternatives but a whole continuum of truth values for logical propositions. A proposition A can have the truth value 0.4 and its complement Ac the truth value 0.5. According to the type of negation operator that is used, the two truth values must not be necessarily add up to 1. Fuzzy logic algorithm can be applied in the diagnosis of ailments such as malaria by evaluating the symptom variables used by doctors to conclude if a patient is sick of malaria [2].
1.1 Background of the Study
Identification of malaria at early stage will be helpful as its effect is increasing drastically and causing harm to people’s life. Malaria is due to imbalance (increase) of amount of parasites in the patient’s blood and an indicator for the degree of infection. Malaria is caused by a blood parasite named Plasmodium spp. It affects at least 300 to 600 million people every year and causes an estimated 3 million deaths. Early diagnosis and treatment of it is necessary. Expert system for disease diagnosis is becoming popular day by day. In today’s world people are so busy, that they hardly have enough time to visit a doctor. So they can use the service of this expert diagnosis system residing home or office and have an idea about the disease. After that they can consult the specialist doctor if it is necessary or serious. Fuzzy logic algorithm can help in the early diagnosis of malaria when applied properly [3].
