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An Ontology Driven Data Mining Approach for CVD Diagnosing among Diabetic Patients in Gaza Strip
Chapter 2 Literature Review11
2.1 Background and literature review:11
2.1.1 Diabetes Mellitus:11
2.1.2 Cardiovascular disease:12
2.1.2.1 Cardiovascular disease Diagnosing:12
2.1.3 Semantic Web (SW):13
2.1.4 Ontology:13
2.1.4.1 Ontology Components:14
2.1.4.2 Ontology Design principles:15
2.1.4.3 Ontology Building Methodologies15
2.1.4.4 Specification stage16
2.1.4.5 Knowledge acquisition stage16
2.1.4.6 Conceptualization stage16
2.1.4.7 Integration stage17
2.1.4.8 Implementation stage17
2.1.4.9 Evaluation stage17
2.1.4.10 Documentation stage18
2.1.5 Data Mining:18
2.1.5.1 Association Rules:18
2.1.5.2 Rule Induction:19
2.1.5.3 Decision Tree:19
2.1.5.4 Performance Evaluation:21
2.1.6 Ontological Data Mining:22
2.1.6.1 Role of Ontological Data Mining:22
2.1.6.2 Fr-ONT-Qu algorithm23
2.2 Related work:24
2.2.1 Disease ontology Construction:24
2.2.2 Data Mining for Diseases diagnosing:26
2.2.3 Ontology and data mining:27
2.2.4 Related Work Conclusion:29
2.3 Summary:31
Chapter 3 Research proposal and Methodology 32
3.1 Methodology steps:32
3.2 Step 1: Collecting informations and Dataset:32
3.3 Step 2: CVD Ontology building phase:34
3.3.1 Step 2.1: Ontology Construction framework:36
3.3.2 Step 2.2: Ontology evaluation:37
3.4 Step 3: Data mining phase37
3.5 Step 4: Ontological Data mining phase:38
3.6 Step 5: Models mapping and Evaluation phase:39
3.7 Summary:39
Chapter 4 Cardiovascular Diseases Ontology Implementation40
1.1 Overview40
1.2 Classification of Cardiovascular disease40
1.3 Ontology classes classification and extraction41
1.4 Defining terms and synonyms related to the topic Classification43
1.5 Building the hierarchy of concepts44
1.6 Relations between concepts and properties44
1.7 Ontology Construction45
1.7.1 Ontology classes in Protégé:46
1.7.2 Define properties of classes-slots46
1.7.3 Define data properties:49
1.7.4 Create instances50
1.7.5 Ontology Reasoner51
1.8 Ontology Evaluation51
1.9 Summary:52
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