Abstract
Data mining technique in the history of medical data found with enormous investigations resulted that the prediction of heart disease is very important in medical science. The data from medical history has been found as heterogeneous data and it seems that the various forms of data should be interpreted to predict the heart disease of a patient. Various techniques in Data Mining have been applied to predict the patients of heart disease. But, the uncertainty in data was not removed with the techniques available in data mining. To remove uncertainty, it has been made an attempt by introducing fuzziness in the measured data. A membership function was designed and incorporated with the measured value to remove uncertainty. Further, an attempt was made to classify the patients based on the attributes collected from medical field. Minimum distance K-NN classifier was incorporated to classify the data among various groups. It was found that Fuzzy K-NN classifier suits well as compared with other classifiers of parametric techniques.
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Krishnaiah, V., Narsimha, G., Chandra, N.S. (2015). Heart Disease Prediction System Using Data Mining Technique by Fuzzy K-NN Approach. In: Satapathy, S., Govardhan, A., Raju, K., Mandal, J. (eds) Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India (CSI) Volume 1. Advances in Intelligent Systems and Computing, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-319-13728-5_42
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DOI: https://doi.org/10.1007/978-3-319-13728-5_42
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