Medical data mining using evolutionary computation.

Abstract

by Ngan Po Shun.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 109-115).Abstract also in Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Data Mining --- p.1Chapter 1.2 --- Motivation --- p.4Chapter 1.3 --- Contributions of the research --- p.5Chapter 1.4 --- Organization of the thesis --- p.6Chapter 2 --- Related Work in Data Mining --- p.9Chapter 2.1 --- Decision Tree Approach --- p.9Chapter 2.1.1 --- ID3 --- p.10Chapter 2.1.2 --- C4.5 --- p.11Chapter 2.2 --- Classification Rule Learning --- p.13Chapter 2.2.1 --- AQ algorithm --- p.13Chapter 2.2.2 --- CN2 --- p.14Chapter 2.2.3 --- C4.5RULES --- p.16Chapter 2.3 --- Association Rule Mining --- p.16Chapter 2.3.1 --- Apriori --- p.17Chapter 2.3.2 --- Quantitative Association Rule Mining --- p.18Chapter 2.4 --- Statistical Approach --- p.19Chapter 2.4.1 --- Chi Square Test and Bayesian Classifier --- p.19Chapter 2.4.2 --- FORTY-NINER --- p.21Chapter 2.4.3 --- EXPLORA --- p.22Chapter 2.5 --- Bayesian Network Learning --- p.23Chapter 2.5.1 --- Learning Bayesian Networks using the Minimum Descrip- tion Length (MDL) Principle --- p.24Chapter 2.5.2 --- Discretizating Continuous Attributes while Learning Bayesian Networks --- p.26Chapter 3 --- Overview of Evolutionary Computation --- p.29Chapter 3.1 --- Evolutionary Computation --- p.29Chapter 3.1.1 --- Genetic Algorithm --- p.30Chapter 3.1.2 --- Genetic Programming --- p.32Chapter 3.1.3 --- Evolutionary Programming --- p.34Chapter 3.1.4 --- Evolution Strategy --- p.37Chapter 3.1.5 --- Selection Methods --- p.38Chapter 3.2 --- Generic Genetic Programming --- p.39Chapter 3.3 --- Data mining using Evolutionary Computation --- p.43Chapter 4 --- Applying Generic Genetic Programming for Rule Learning --- p.45Chapter 4.1 --- Grammar --- p.46Chapter 4.2 --- Population Creation --- p.49Chapter 4.3 --- Genetic Operators --- p.50Chapter 4.4 --- Evaluation of Rules --- p.52Chapter 5 --- Learning Multiple Rules from Data --- p.56Chapter 5.1 --- Previous approaches --- p.57Chapter 5.1.1 --- Preselection --- p.57Chapter 5.1.2 --- Crowding --- p.57Chapter 5.1.3 --- Deterministic Crowding --- p.58Chapter 5.1.4 --- Fitness sharing --- p.58Chapter 5.2 --- Token Competition --- p.59Chapter 5.3 --- The Complete Rule Learning Approach --- p.61Chapter 5.4 --- Experiments with Machine Learning Databases --- p.64Chapter 5.4.1 --- Experimental results on the Iris Plant Database --- p.65Chapter 5.4.2 --- Experimental results on the Monk Database --- p.67Chapter 6 --- Bayesian Network Learning --- p.72Chapter 6.1 --- The MDLEP Learning Approach --- p.73Chapter 6.2 --- Learning of Discretization Policy by Genetic Algorithm --- p.74Chapter 6.2.1 --- Individual Representation --- p.76Chapter 6.2.2 --- Genetic Operators --- p.78Chapter 6.3 --- Experimental Results --- p.79Chapter 6.3.1 --- Experiment 1 --- p.80Chapter 6.3.2 --- Experiment 2 --- p.82Chapter 6.3.3 --- Experiment 3 --- p.83Chapter 6.3.4 --- Comparison between the GA approach and the greedy ap- proach --- p.91Chapter 7 --- Medical Data Mining System --- p.93Chapter 7.1 --- A Case Study on the Fracture Database --- p.95Chapter 7.1.1 --- Results of Causality and Structure Analysis --- p.95Chapter 7.1.2 --- Results of Rule Learning --- p.97Chapter 7.2 --- A Case Study on the Scoliosis Database --- p.100Chapter 7.2.1 --- Results of Causality and Structure Analysis --- p.100Chapter 7.2.2 --- Results of Rule Learning --- p.102Chapter 8 --- Conclusion and Future Work --- p.106Bibliography --- p.109Chapter A --- The Rule Sets Discovered --- p.116Chapter A.1 --- The Best Rule Set Learned from the Iris Database --- p.116Chapter A.2 --- The Best Rule Set Learned from the Monk Database --- p.116Chapter A.2.1 --- Monkl --- p.116Chapter A.2.2 --- Monk2 --- p.117Chapter A.2.3 --- Monk3 --- p.119Chapter A.3 --- The Best Rule Set Learned from the Fracture Database --- p.120Chapter A.3.1 --- Type I Rules: About Diagnosis --- p.120Chapter A.3.2 --- Type II Rules : About Operation/Surgeon --- p.120Chapter A.3.3 --- Type III Rules : About Stay --- p.122Chapter A.4 --- The Best Rule Set Learned from the Scoliosis Database --- p.123Chapter A.4.1 --- Rules for Classification --- p.123Chapter A.4.2 --- Rules for Treatment --- p.126Chapter B --- The Grammar used for the fracture and Scoliosis databases --- p.128Chapter B.1 --- The grammar for the fracture database --- p.128Chapter B.2 --- The grammar for the Scoliosis database --- p.12