TABLE OF CONTENTS
CHAPTER ONE: INTRODUCTION
1.1. Background to the Study
1.2. Statement of the Problem `
1.3. Objective of the Study
1.4. Methodology
1.5. Scope of the Study
1.6. Significance of Study
1.7. Organization of the Subsequent Chapters
CHAPTER TWO: REVIEW OF LITERATURE
2.0. Introduction
2.1. Clinical Decision-Making
2.1.1. Clinical Decision Analysis (Problem Solving)
2.1.2. Structure of Clinical Decision Support Systems
2.1.3. Elements of CDSS Design and Function
2.1.4. CDSS Knowledge Axes
2.1.5. CDSS Decision support Axes
2.1.6. CDSS Information Delivery Axes
2.1.7. CDSS Model
2.1.8. CDSS Knowledge Representation
2.1.9. Knowledge Engineer
2.1.10. CDSS Knowledge Acquisition 2.1.11. CDSS User Interface
2.2. Overview of Diabetes Neuropathy 2.2.1. Causes of Diabetes Neuropathy
2.2.2. Symptoms of Diabetes Neuropathy
2.2.3. Classification of Diabetes Neuropathy
2.2.4. Diagnosis of Diabetes Neuropathy
2.2.5. Early Detection of Diabetes Neuropathy
2.3. Review of Closely Related Works 2.3.1. A Novel Analysis of Diabetes Mellitus by Using Expert System
Based on Brain Derived Neurotropic Factor (BDNF) Levels 2.3.2. Web Based Intelligent Decision Support System for Type 2
Diabetes Patients 2.3.3. An Expert System for Diabetes Diagnosis using VP_Expert Shell
2.3.4. Web Based Medical Diagnosis System Using ANN-ARM
For Diabetes Mellitus
2.3.5. Expert System for Diagnosis and treatment of Diabetes
2.3.6. Design of a Diabetic Diagnosis Expert System Using Rough Sets
2.3.7. Mycin 2.3.8. Gideion
Content
2.3.9. An Expert System for Diagnosing Diseases and Prescribing
Medication Using Visual Basic.Net (Autodoc)
2.3.10. Computerized clinical decision support systems for chronic disease
management: A decision-maker-researcher partnership systematic review
2.3.11. Clinical Decision Support System for Diabetes Disease Diagnosis using
fuzzy logic
2.4. The limitations of related works reviewed
2.5. Pattern Classification
2.5.1. Approach to Pattern Classification
2.6. Classification Algorithms
2.6.1 Types of Classification Algorithms
2.7. K-nearest Neighbors (KNN)
2.8. Decision Tree Algorithm (DTA)
2.9. Decision Stump (DS)
2.10. Rule Induction (RI)
CHAPTER THREE: METHODOLOGY
3.0. Introduction
3.1. Design a Web-based Clinical Decision Support System to Diagnose
and Manage Diabetes Neuropathy
3.2. Research Tools
3.2.1. Wamp Package
3.2.2. Adobe Dreamweaver Creative Suite 6
3.2.3. Software Development Life Cycle
3.2.4. Spiral Model
3.2.5. History of Spiral Model
3.3. Clinical Decision Support System (CDSS) Modules
3.3.1. User Interface
3.3.2. Medical Knowledge Base
3.3.3. Classification Algorithm Module 3.3.4. Inference Engine
3.3.5. Database Design 3.3.6. Tables 3.3.7. Use Case Diagram for the System
3.3.8. Sequence Diagram
3.3.9. Activity Diagram
3.4. Data Acquisition
3.4.1. Preparation of Acquired Dataset
3.4.2. Rapid Miner Software Version 6.2
3.5. Classification of Dataset using the Naïve Bayes Theorem
3.6. Naïve Bayes 3.6.1. How Naïve Bayes Theorem Works