Comparative Analysis of Data Mining Classification Techniques for Prediction of Heart Disease Using the Weka and SPSS Modeler Tools

  • Atul Kumar RamotraEmail author
  • Amit Mahajan
  • Rakesh Kumar
  • Vibhakar Mansotra
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 165)


The healthcare sector generates enormous data related to electronic medical records containing detailed reports, test, and medications. Research in the field of health care is being carried out to utilize the available healthcare data effectively using data mining. Every year, heart disease causes millions of deaths around the world. This research paper intends to analyze a few important parameters and utilize data mining classification techniques to predict the presence of heart disease. Data mining techniques are very useful in identifying the hidden patterns and information in the dataset. Decision Tree, Naïve Bayes, Support Vector Machines, and Artificial Neural Networks classifiers are used for the prediction in Weka and SPSS Modeler tools and comparison of results is done on the basis of sensitivity, specificity, precision, and accuracy. Naive Bayes classifier achieved the highest accuracy of 85.39% in the Weka tool, and in the SPSS Modeler tool, SVM classifier achieved the highest accuracy at 85.87%.


Data mining Classification Heart disease Decision tree Naïve Bayes Support vector machines k-Nearest Neighbor Artificial neural networks Weka SPSS Modeler Sensitivity Selectivity Prediction Accuracy 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Atul Kumar Ramotra
    • 1
    Email author
  • Amit Mahajan
    • 1
  • Rakesh Kumar
    • 1
  • Vibhakar Mansotra
    • 1
  1. 1.Department of Computer Science & ITUniversity of JammuJammuIndia

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