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A Heart Disease Prediction Model Using Decision Tree

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  • Atul Kumar Pandey
  • Prabhat Pandey
  • K L Jaiswal

Abstract

In this paper, we develop a heart disease prediction model that can assist medical professionals in predicting the heart disease status based on the clinical data of patients. First, we select 14 important clinical features; second, we develop a prediction model using J48 decision tree for classifying heart disease based on these clinical features against unpruned, pruned, and pruned with reduced error pruning approach. Finally, it is found that the accuracy of pruned J48 decision tree with reduced error pruning approach is better than the simple pruned and unpruned approach. The results obtained show that fasting blood sugar is the most important attribute which gives better classification against the other attributes but does not give better accuracy.

Suggested Citation

  • Atul Kumar Pandey & Prabhat Pandey & K L Jaiswal, 2013. "A Heart Disease Prediction Model Using Decision Tree," The IUP Journal of Computer Sciences, IUP Publications, vol. 0(3), pages 43-48, July.
    • Handle: RePEc:icf:icfjcs:v:7:y:2013:i:3:p:43-48
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    Cited by:

    1. Luis Rolando Guarneros-Nolasco & Nancy Aracely Cruz-Ramos & Giner Alor-Hernández & Lisbeth Rodríguez-Mazahua & José Luis Sánchez-Cervantes, 2021. "Identifying the Main Risk Factors for Cardiovascular Diseases Prediction Using Machine Learning Algorithms," Mathematics, MDPI, vol. 9(20), pages 1-25, October.

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