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Prediction of heart disease using data mining techniques

  • Special Issue REDSET 2016 of CSIT
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Abstract

The healthcare industry is a vast field with a plethora of data about patients,added to the huge medical records every passing day. In terms of science, this industry is ’information rich’ yet ’knowledge poor’. However, data mining with its various analytical tools and techniques plays a major role in reducing the use of cumbersome tests used on patients to detect a disease. The aim of this paper is to employ and analyze different data mining techniques for the prediction of heart disease in a patient through extraction of interesting patterns from the dataset using vital parameters. This paper strives to bring out the methodology and implementation of these techniques-Artificial Neural Networks, Decision Tree and Naive Bayes and stress upon the results and conclusion induced on the basis of accuracy and time complexity. By far, the observations reveal that Artificial Neural Networks outperformed Naive Bayes and Decision Tree.

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Authors and Affiliations

  1. Manipal Institute of Technology, Manipal, Karnataka, India

    Ritika Chadha & Shubhankar Mayank

Authors
  1. Ritika Chadha
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  2. Shubhankar Mayank
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Correspondence to Ritika Chadha.

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Chadha, R., Mayank, S. Prediction of heart disease using data mining techniques. CSIT 4, 193–198 (2016). https://doi.org/10.1007/s40012-016-0121-0

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  • DOI: https://doi.org/10.1007/s40012-016-0121-0

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