Application of a fuzzy discrimination analysis for diagnosis of valvular heart disease

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Abstract
We have applied the discrimination analysis proposed by Norris, Pilsmorth, and Baldwin to the diagnosis of valvular heart diseases. They proposed the diagnosis method which uses concepts from fuzzy set theory. It consists of two independent parts: discrimination analysis and connectivity analysis. We performed the experiments in order to evaluate the effectiveness of the proposed discrimination analysis part of the method. Also, we extended the original method to handle partial manifestation of symptoms and severity of diseases by using fuzzy sets. In addition, we introduced the concept of prototypicalness of patients with a particular disease to improve the performance of the diagnosis. The results of the experiments are very promising. In the best case, we achieved a rate of true positive diagnosis of 81% while maintaining a rate of false positive diagnosis at the low level of 10%. We report the quantitative results of the experiments.<>