Diagnosis of heart disease patients using fuzzy classification technique

Data mining technique in the history of medical data found with enormous investigations found that the prediction of heart disease is very important in medical science. In medical history it is observed that the unstructured data as heterogeneous data and it is observed that the data formed with different attributes should be analyzed to predict and provide information for making diagnosis of a heart patient. Various techniques in Data Mining have been applied to predict the heart disease patients. But, the uncertainty in data was not removed with the techniques available in data mining and implemented by various authors. To remove uncertainty of unstructured data, an attempt was made by introducing fuzziness in the measured data. A membership function was designed and incorporated with the measured value to remove uncertainty and fuzzified data was used to predict the heart disease patients.. Further, an attempt was made to classify the patients based on the attributes collected from medical field. Minimum Euclidean distance Fuzzy K-NN classifier was designed to classify the training and testing data belonging to different classes. It was found that Fuzzy K-NN classifier suits well as compared with other classifiers of parametric techniques.