Extraction of association rules in medical datasets via multi-objective genetic algorithms

Medical data are facing a challenge of knowledge discovery from the growing volume of data. Nowadays enormous amounts of information are continuously collected by monitoring physiological parameters of patients. The growing amounts of data has made manual analysis by medical experts a tedious task and sometimes impossible. Many hidden and potentially useful relationships may not be recognized by the analyst. The explosive growth of data requires an automated way in order to extract useful knowledge. One of the possible approaches of this problem is data mining or knowledge discovery from databases. Through data mining, interesting knowledge and regularities can be extracted and the discovered knowledge can be applied in the corresponding field to increase the work efficiency and in order to improve the quality of decision making. The objectives of the thesis are to extract association and classification rules from the schizophrenia medical data. Association and classification rules are typically useful for medical problems which have been massively applied particularly in the area of medical diagnosis. Such rules can be verified by medical experts and provide better understanding of the problem in-hand. Numerous techniques have been applied to rule discovering in data mining over the past decades, such as expert systems, artificial neural networks, linear programming, database systems and evolutionary algorithms. Among these approaches, the evolutionary algorithms have been emerged as promising techniques in dealing with the increasing challenge of data mining in medical area. The evolutionary algorithm is a class of computational techniques inspired by the natural evolution process that imitates the mechanism of the natural selection and survival-of-the-fittest in solving real life problems.In this thesis, the aim is to discover classification and association rules via multi-objective evolutionary algorithms. Contrary to single-objective ones, multi-objective evolutionary algorithms deal with simultaneous optimization of several incommensurable and often competing objectives.