Novel Hybrid Decision Support Disease Diagnosis Systems Usin

发布时间：2021-04-21 20:40

　　The amount of patient’s data in the healthcare facilities are increasing rapidly for the past few decades.The challenge is how to analyze available patient’s data to extract relevant knowledge from it and act upon it,in a timely manner.Efficient data mining tools must be utilized to turn the data into knowledge which can aid developing decision-based expert systems that will assist physicians in the early diagnosis of lethal diseases.Such expert systems can reduce human-made errors and mistakes（... 

【文章来源】：浙江理工大学浙江省

【文章页数】：87 页

【学位级别】：硕士

【文章目录】：
PUBLICATIONS
ACKNOWLEDGEMENTS
Abstract
List of Abbreviations/Acronyms
Chapter 1 Introduction
    1.1. Overview
        1.1.1. Hepatitis Diseases
        1.1.2. Thyroid Disease
    1.2. Problem Statement
    1.3. Research Objectives
        1.3.1. General Objective
        1.3.2. Specific Objectives
        1.3.3. Research Question
    1.4. Justification of The Proposed Approaches
    1.5. Scope of The Proposed Approaches
    1.6. Summary
Chapter 2 Literature Review
    2.1. Overview （Datamining; Definition and Concept）
    2.2. Data Mining as a Process
    2.3. Machine Learning Techniques
        2.3.1. Na?ve Bayes Classifier （NBC）
        2.3.2. Decision Tree
        2.3.3. Artificial Neural Network （ANN）
    2.4. Classification Methods
        2.4.1. Adaptive Neuro-Fuzzy Inference System （ANFIS）
    2.5. Feature Extraction techniques
        2.5.1. Principal Component Analysis （PCA）
        2.5.2. Symmetrical Uncertainty （SU）
        2.5.3. Relief
        2.5.4. Correlation-Based Features Selection
        2.5.5. Focus
        2.5.6. Las Vegas Filter
        2.5.7. Information Gain
        2.5.8. Linear Discriminant Analysis
    2.6. Handling Incomplete Data
        2.6.1. Weighed based pre-processing using k NN algorithm
    2.7. Metrics
    2.8. Diagnosis History of Experimented Diseases
        2.8.1. Hepatitis Disease Diagnosis Background
        2.8.2. Thyroid Disease Diagnosis Background
    2.9. Summary
Chapter 3 Materials and Methods
    3.1. Overview
    3.2. Data mining
        3.2.1. Data Retrieval
        3.2.2. Feature Extraction
        3.2.3. Data Preprocessing
        3.2.4. Applying Proposed Approaches
        3.2.5. Evaluation
        3.2.6. Results
    3.3. Summary
Chapter 4 Experimental results and Discussion
    4.1. Overview
    4.2. Presentation of Data
    4.3. Results Discussion
        4.3.1. IG-KNN-ANFIS Results
        4.3.2. IG-ANFIS Results
        4.3.3. LDA-KNN-ANFIS Results
    4.4. Summary
Chapter 5 Conclusion and Future Work
    5.1. Overview
    5.2. conclusion
    5.3. Future Work
References



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