A Neuro Fuzzy Decision Tree Model for Predicting the Risk in Coronary Artery Disease

The application of a neuro fuzzy model, typically a TSK model that incorporates rule structures obtained from the classical ID3 approach of decision trees in predicting the degree of risks from the information obtained through clinical observations in coronary artery disease patients is discussed in this paper. In recent years, numerous attempts have been made to use knowledge structures represented by fuzzy systems and artificial neural networks in various applications particularly in decision-making models. The utility of fuzzy systems lies in their ability for modeling uncertain or ambiguous, multi-parameter data often encountered in complex situations like medical diagnosis. This paper proposes a new model for medical decision making situations.