Discovering Multiple Diagnostic Rules from Coronary Heart Disease Database using Automatically Defined Groups
Much of the research on extracting rules from a large amount of data has focused on the extraction of a general rule that covers as many data as possible. In the field of health care, where people’s lives are at stake, it is necessary to diagnose appropriately without overlooking the small number of patients who show different symptoms. Thus, the exceptional rules for rare cases are also important. From such a viewpoint, multiple rules, each of which covers a part of the data, are needed for covering all data. In this paper, we describe the extraction of such multiple rules, each of which is expressed by a tree structural program. We consider a multi-agent approach to be effective for this purpose. Each agent has a rule that covers a part of the data set, and multiple rules which cover all data are extracted by multi-agent cooperation. In order to realize this approach, we propose a new method for rule extraction using Automatically Defined Groups (ADG). The ADG, which is based on Genetic Programming, is an evolutionary optimization method of multi-agent systems. By using this method, we can acquire both the number of necessary rules and the tree structural programs which represent these respective rules. We applied this method to a database used in the machine learning field and showed its effectiveness. Moreover, we applied this method to medical data and developed a diagnostic system for coronary heart diseases