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Comparison of Different Decision Tree Algorithms for Predicting the Heart Disease

  • Deepak SaraswatEmail author
  • Preetvanti Singh
Conference paper
  • 57 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1241)

Abstract

Data mining procedures are utilized to extract meaningful information for effective knowledge discovery. Decision tree, a classification method, is an efficient method for prediction. Seeing its importance, this paper compares decision tree algorithms to predict heart disease. The heart disease data sets are taken from Cleveland database, Hungarian database and Switzerland database to evaluate the performance measures. 60 data records for training and 50 data records for testing were taken as input for comparison. In order to evaluate the performance, fourteen attributes are considered to generate confusion matrices. The results exhibited that the algorithm that highest accuracy rates for predicting heart disease is Random forest, and thus can be considered as the best procedure for prediction.

Keywords

Heart disease Classification technique Decision tree Decision tree algorithms Performance measures 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Physics and Computer ScienceDayalbagh Educational InstituteAgraIndia

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