Host: Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)
In this paper, we propose learning function for an agent acquires characteristic of this data aggregate autonomously so that an assortment technique by the artificial neural network which used revolving underwriting facility aggregation theory for defect and a reliability enhancement of a diagnostic information realizes high prediction accuracy when we use clinical practice diagnostic data from conventional research result by this case study, and it to be possible for diagnosis support system. We aim at three building of function generating the data set which necessary an agent used to the artificial neural network, and to learn medical diagnostic data (prostate cancer, kidney cancer, bladder cancer) by revolving underwriting facility aggregation theory handled at the urology department to be concrete.
