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Improving the Accuracy for Analyzing Heart Diseases Prediction Based on the Ensemble Method

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  • Xiao-Yan Gao
  • Abdelmegeid Amin Ali
  • Hassan Shaban Hassan
  • Eman M. Anwar
  • Ahmed Mostafa Khalil

Abstract

Heart disease is the deadliest disease and one of leading causes of death worldwide. Machine learning is playing an essential role in the medical side. In this paper, ensemble learning methods are used to enhance the performance of predicting heart disease. Two features of extraction methods: linear discriminant analysis (LDA) and principal component analysis (PCA), are used to select essential features from the dataset. The comparison between machine learning algorithms and ensemble learning methods is applied to selected features. The different methods are used to evaluate models: accuracy, recall, precision, F-measure, and ROC.The results show the bagging ensemble learning method with decision tree has achieved the best performance.

Suggested Citation

  • Xiao-Yan Gao & Abdelmegeid Amin Ali & Hassan Shaban Hassan & Eman M. Anwar & Ahmed Mostafa Khalil, 2021. "Improving the Accuracy for Analyzing Heart Diseases Prediction Based on the Ensemble Method," Complexity, Hindawi, vol. 2021, pages 1-10, February.
    • Handle: RePEc:hin:complx:6663455
    DOI: 10.1155/2021/6663455
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