Medical data mining using Bayesian network and DNA sequence analysis.

• Other Authors: Lee, Kit Ying.
• Format: Others
• Language: English; Chinese
• Published: 2004
• Subjects: Data mining; Medical informatics; Nucleotide sequence; Information Storage and Retrieval; Base Sequence; Bayes Theorem
• Online Access: http://library.cuhk.edu.hk/record=b5892079; http://repository.lib.cuhk.edu.hk/en/item/cuhk-324927

Lee Kit Ying. === Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. === Includes bibliographical references (leaves 115-117). === Abstracts in English and Chinese. === Abstract --- p.i === Acknowledgement --- p.iv === Chapter 1 --- Introduction --- p.1 === Chapter 1.1 --- Project Background --- p.1 === Chapter 1.2 --- Problem Specifications --- p.3 === Chapter 1.3 --- Contributions --- p.5 === Chapter 1.4 --- Thesis Organization --- p.6 === Chapter 2 --- Background --- p.8 === Chapter 2.1 --- Medical Data Mining --- p.8 === Chapter 2.1.1 --- General Information --- p.9 === Chapter 2.1.2 --- Related Research --- p.10 === Chapter 2.1.3 --- Characteristics and Difficulties Encountered --- p.11 === Chapter 2.2 --- DNA Sequence Analysis --- p.13 === Chapter 2.3 --- Hepatitis B Virus --- p.14 === Chapter 2.3.1 --- Virus Characteristics --- p.15 === Chapter 2.3.2 --- Important Findings on the Virus --- p.17 === Chapter 2.4 --- Bayesian Network and its Classifiers --- p.17 === Chapter 2.4.1 --- Formal Definition --- p.18 === Chapter 2.4.2 --- Existing Learning Algorithms --- p.19 === Chapter 2.4.3 --- Evolutionary Algorithms and Hybrid EP (HEP) --- p.22 === Chapter 2.4.4 --- Bayesian Network Classifiers --- p.25 === Chapter 2.4.5 --- Learning Algorithms for BN Classifiers --- p.32 === Chapter 3 --- Bayesian Network Classifier for Clinical Data --- p.35 === Chapter 3.1 --- Related Work --- p.36 === Chapter 3.2 --- Proposed BN-augmented Naive Bayes Classifier (BAN) --- p.38 === Chapter 3.2.1 --- Definition --- p.38 === Chapter 3.2.2 --- Learning Algorithm with HEP --- p.39 === Chapter 3.2.3 --- Modifications on HEP --- p.39 === Chapter 3.3 --- Proposed General Bayesian Network with Markov Blan- ket (GBN) --- p.40 === Chapter 3.3.1 --- Definition --- p.41 === Chapter 3.3.2 --- Learning Algorithm with HEP --- p.41 === Chapter 3.4 --- Findings on Bayesian Network Parameters Calculation --- p.43 === Chapter 3.4.1 --- Situation and Errors --- p.43 === Chapter 3.4.2 --- Proposed Solution --- p.46 === Chapter 3.5 --- Performance Analysis on Proposed BN Classifier Learn- ing Algorithms --- p.47 === Chapter 3.5.1 --- Experimental Methodology --- p.47 === Chapter 3.5.2 --- Benchmark Data --- p.48 === Chapter 3.5.3 --- Clinical Data --- p.50 === Chapter 3.5.4 --- Discussion --- p.55 === Chapter 3.6 --- Summary --- p.56 === Chapter 4 --- Classification in DNA Analysis --- p.57 === Chapter 4.1 --- Related Work --- p.58 === Chapter 4.2 --- Problem Definition --- p.59 === Chapter 4.3 --- Proposed Methodology Architecture --- p.60 === Chapter 4.3.1 --- Overall Design --- p.60 === Chapter 4.3.2 --- Important Components --- p.62 === Chapter 4.4 --- Clustering --- p.63 === Chapter 4.5 --- Feature Selection Algorithms --- p.65 === Chapter 4.5.1 --- Information Gain --- p.66 === Chapter 4.5.2 --- Other Approaches --- p.67 === Chapter 4.6 --- Classification Algorithms --- p.67 === Chapter 4.6.1 --- Naive Bayes Classifier --- p.68 === Chapter 4.6.2 --- Decision Tree --- p.68 === Chapter 4.6.3 --- Neural Networks --- p.68 === Chapter 4.6.4 --- Other Approaches --- p.69 === Chapter 4.7 --- Important Points on Evaluation --- p.69 === Chapter 4.7.1 --- Errors --- p.70 === Chapter 4.7.2 --- Independent Test --- p.70 === Chapter 4.8 --- Performance Analysis on Classification of DNA Data --- p.71 === Chapter 4.8.1 --- Experimental Methodology --- p.71 === Chapter 4.8.2 --- Using Naive-Bayes Classifier --- p.73 === Chapter 4.8.3 --- Using Decision Tree --- p.73 === Chapter 4.8.4 --- Using Neural Network --- p.74 === Chapter 4.8.5 --- Discussion --- p.76 === Chapter 4.9 --- Summary --- p.77 === Chapter 5 --- Adaptive HEP for Learning Bayesian Network Struc- ture --- p.78 === Chapter 5.1 --- Background --- p.79 === Chapter 5.1.1 --- Objective --- p.79 === Chapter 5.1.2 --- Related Work - AEGA --- p.79 === Chapter 5.2 --- Feasibility Study --- p.80 === Chapter 5.3 --- Proposed A-HEP Algorithm --- p.82 === Chapter 5.3.1 --- Structural Dissimilarity Comparison --- p.82 === Chapter 5.3.2 --- Dynamic Population Size --- p.83 === Chapter 5.4 --- Evaluation on Proposed Algorithm --- p.88 === Chapter 5.4.1 --- Experimental Methodology --- p.89 === Chapter 5.4.2 --- Comparison on Running Time --- p.93 === Chapter 5.4.3 --- Comparison on Fitness of Final Network --- p.94 === Chapter 5.4.4 --- Comparison on Similarity to the Original Network --- p.95 === Chapter 5.4.5 --- Parameter Study --- p.96 === Chapter 5.5 --- Applications on Medical Domain --- p.100 === Chapter 5.5.1 --- Discussion --- p.100 === Chapter 5.5.2 --- An Example --- p.101 === Chapter 5.6 --- Summary --- p.105 === Chapter 6 --- Conclusion --- p.107 === Chapter 6.1 --- Summary --- p.107 === Chapter 6.2 --- Future Work --- p.109 === Bibliography --- p.117