CHAPTER ONE: INTRODUCTION
1.1 Background of The Study 1
1.2 Statement of The Problem 3
1.3 Aim and Objectives of The Study 3
1.4 Research Questions 4
1.5 Scope of The Study 4
1.6 Research Methodology 4
1.7 Significance of The Study 5
1.8 Limitations of The Study 6
CHAPTER TWO: LITERATURE REVIEW
2.1 Overview on Meningitis 7
2.1.1 How Meningitis is Transmitted 8
2.1.2 Symptoms of Meningitis 9
2.1.3 Risk Factors for Meningitis 9
2.1.5 How is Meningitis Treated 11
2.1.6 Prevention of Meningitis 11
2.2 Anomaly Detection 13
2.2.1 Rule Based Expert Systems (RBR) 13
2.2.1.1 Theory of Rule-Based Systems 14
2.2.1.2 Rule-Based Reasoning 15
2.3 Overview on Expert Advisor System 16
2.3.1 The Structure of Medical Expert Systems 17
2.3.2 Rule Based Expert System 18
2.3.3 Knowledge Based Expert System 18
2.4 Artificial Intelligence (AI) and Clinical Guidelines 19
2.4.1 Group Decision Making 19
2.5 Overview On Expert System 20
2.6 Computer-Interpretable Guidelines 21
2.6.1 Clinical Expert Systems 22
2.6.2 Importance of Clinical Expert Systems 23
2.6.3 Clinician Motivation To Use Expert Systems 24
2.7 The Concept on Rule Based Approach in Expert System 25
2.7.1 Obtaining Information for Developing Rules 26
CHAPTER THREE: SYSTEM ANALYSIS AND DESIGN
3.1 Analysis of The Existing System 27
3.2 Problem with The Existing System 28
3.3 Analysis of The Proposed System 28
3.4 Benefits of The Proposed System 29
3.5 System Design 29
3.5.1 Preliminary Design Stage 29
3.5.2 Structural Design Stage 30
3.5.3 Program Design 30
3.5.4 Interface Design 30
3.5.5 Input/Output Design 30
3.5.6 Input Design 31
3.5.7 Output Design 35
3.6 Data Flow Diagram 37
3.7 Database and Structure of Table 39
3.8. Entity Relationship Diagrams 42
3.9 Proposed System with UML 43
3.10 The Algorithm of Rule Based Expert System 47
CHAPTER FOUR: PROGRAMMING AND DOCUMENTATION
4.1 System Implementation 48
4.1.1 Functional Requirement 48
4.1.2 Non-Functional Requirement 48
4.2 Justification of Programming Language Used 50
4.3 Operating Procedures 51
4.3.1 Program Installation 51
4.3.2 Programmer’s Guide to Maintenance 51
4.3.3 Personnel and Procedure 52
4.4 User Guide to The System 52
4.4.1 Running the Program 52
4.4.2 Program Specification and Sub Programs 52
4.4.3 Change over process 53
4.5 Documentation of The System 53
4.6 Statistical Analysis 54
4.7 Hypothesis Formulation/Result and Discussion 54
4.7.1 Hypothesis Testing 57
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATION
5.1 Summary 60
5.2 Conclusion 61
5.3 Recommendation 62
References 69
Detection of diseases are important, so as to stop them from infecting us. More so, outbreak diseases which cause epidemics. Traditional techniques for anomaly detection are unsatisfactory for this problem because they identify individual data points that are rare due to particular combinations of features. When applied to our scenario, these traditional algorithms discover isolated outliers of particularly strange events, such as an irrelevant event, that are not indicative of a new outbreak. Instead, we would like to detect anomalous patterns. These patterns are groups with specific characteristics whose recent pattern of illness is anomalous relative to historical patterns. We used a rule based anomaly detection algorithm that characterizes each anomalous pattern with a rule. The significance of each rule was carefully evaluated. Our algorithm is compared against a standard detection algorithm by measuring the number of false positives and the timeliness of detection. Simulated data, produced by a questionnaire that creates the effects of an epidemic on the respondent location, would be used for evaluation. The results outcomes indicate that our algorithm has significantly better detection times for common significance thresholds on meningitis.
