Abstract:
In this paper, a Fuzzy-Classified Neural learning soft computing tool (FCNL)
is proposed for predicting the intensity of risk in Coronary Heart occurrences.
The presented model utilizes medical data collected from clinical findings on
cardiac patients. The concept of decision trees is employed to classify the
attributes that add to the Coronary Artery Disease (CAD). The output obtained
as a result is then transformed to crisp if-then rules and then fuzzified into a
database of fuzzy rules. A fuzzy-classified neural learning method based on
supervised learning is exercised to enhance fuzzy membership functions. The
performance and efficiency of the new medical data mining system, in terms
of accuracy of prediction is presented against the real-life data.
