Abstract

The institute of medicine has long recognized Problems with health care quality and for more than a decade has advocated using health information technology to improve quality. The fuzzy cognitive map has gradually emerged as a concrete paradigm for knowledge representations, and simulation conditions are applicable, to numerous research and application field. However we have proposed efficient methods to determine clustering based investigated systems, for medical decision making. The manually developed models have a substantially shortcoming due to difficulties in assessing its reliability. In this paper we proposed a fuzzy logical network that enhances the learning ability of FCM. Our approach discusses the inference mechanisms of conventional FCM in the determination of membership functions. FCM models of investigations system can be automatically constructed from medical data using our approach. In the employed fuzzy logical networks the concept of mutual subset used to describe the casualties which provide more transparent interpretations in FCM. The effectiveness of the proposed approach in prediction of jaundice using clustering is demonstrated through numerical simulation.

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