A Comparative Study on Naïve Bayes and Bayes Net Classifiers for the Heart Disease Prediction System

V Sugumaran, V. Sabarinathan, K. Rajesh Khanna, V. J. Sarath Kumar

Abstract


Cardio vascular diseases are the leading cause of death in the recent years. Large volume of medical data which are available in health care is used to identify the hidden information. From a set of symptoms, one should be able to predict the heart diseases. Here, a predictive model is built using the machine learning approach. This is done in two steps; firstly, feature selection is done through decision tree. Then comparative study on Naïve Bayes and Bayes net classifiers is discussed. Bayes classifiers are built using conditional probability and hence, it requires less number of data points for training. The results show that Bayes net gives maximum of 85% accuracy with three attributes whereas Naïve Bayes yields maximum of 85% with all the 13 attributes. The suitable algorithm is presented in the conclusion.

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References


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