A comparative study of fuzzy classifiers on heart data

Fuzzy approaches can play an important role in data mining, because they provide comprehensible results. In addition, the approaches studied in data mining have mainly been oriented at highly structured and precise data. In this paper, we examine the performance of four fuzzy classifiers on heart data. The fusion of Fuzzy Logic with the classifiers Decision Trees, K-means, Naïve bayes and neural network are used to evaluate the accuracy of occurrence of a heart disease. The experiments are carried out on heart data set of UCI machine learning repository and it is implemented on MATLAB.