A Classification Method of Coronary Heart Disease Databases by Clonal Selection Algorithm with Immunological Memory Cell

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  • ICHIMURA Takumi
    Faculty of Management and Information Systems, Prefectural University of Hiroshima
  • KAMADA Shin
    Program in Management and Information Systems, Graduate School of Comprehensive Scientific Research, Prefectural University of Hiroshima

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The Coronary Heart Disease Database (CHD_DB) is based on actual measurements of the Framingham Heart Study - one of the most famous prospective studies of cardiovascular disease. The CHD_DB includes more than 6,500 records indicating the development of coronary heart disease (CHD). The data set is statistically proved to be an effective as a benchmark test. This paper describes the classification result of CHD_DB by using some learning methods. Especially, a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response was well discussed. Antibodies generated by the clonal selection algorithm are clustered in some categories according to the affinity maturation, so that immunological memory cells which respond to the specified pathogen are created to record those antibodies. Such observations supply the inspiration for the Clonal Selection Algorithm with Immunological Memory Cells. For the data set, our proposed method shows about 99.6% classification capability of training data.

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