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A Predictive Model for Heart Disease Diagnosis Using Fuzzy Logic and Decision Tree

  • Asim Kumar PathakEmail author
  • J. Arul Valan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 767)

Abstract

Heart disease is one of the preeminent causes of death among people all over the world. Heart disease diagnosis procedure is a major and critical issue for the medical practitioner. Diagnosing the heart disease with the predictive model will have a tremendous effect on health care. Researchers have developed many predictive models and expert system using machine learning and data mining techniques for the diagnosis of heart disease. In this study, we propose a predictive model for heart disease diagnosis using a fuzzy rule-based approach with decision tree. In the proposed work, we have obtained the accuracy of 88% which is statistically significant for diagnosing the heart disease patient and also outperforms some of the existing methods. The proposed model uses only eight attributes of a patient to diagnose the heart disease, which is also a major advantage of our proposed model.

Keywords

Heart disease Decision tree Fuzzy inference system Predictive model 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of CSEBIT SindriJharkhandIndia
  2. 2.Department of CSENIT NagalandNagalandIndia

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