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

Authors

  • V Sugumaran Associate Professor, School of Mechanical Engineering and Building Sciences, VIT University
  • V. Sabarinathan School of Information Technology, SRM University, Chennai, India
  • K. Rajesh Khanna School of Mechanical and Building Science, VIT University, Chennai, India
  • V. J. Sarath Kumar School of Electrical Engineering, SRM University, Chennai, India

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.

Author Biographies

V Sugumaran, Associate Professor, School of Mechanical Engineering and Building Sciences, VIT University

Associate Professor, School of Mechanical Engineering and Building Sciences, VIT University

V. Sabarinathan, School of Information Technology, SRM University, Chennai, India

School of Information Technology, SRM University, Chennai, India

K. Rajesh Khanna, School of Mechanical and Building Science, VIT University, Chennai, India

School of Mechanical and Building Science, VIT University, Chennai, India

V. J. Sarath Kumar, School of Electrical Engineering, SRM University, Chennai, India

School of Electrical Engineering, SRM University, Chennai, India

References

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Published

2015-06-11

Issue

Vol. 1 No. 2 (2016)

Section

Articles