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Heart Diseases (HD) lead to a high rate of mortality, so are considered major health problem in the population. Therefore, early diagnosis of these diseases is very important. This paper proposes deep learning-based model MLP (Multilayer Perceptron) for predicting heart or cardiac disease. The performance of MLP was compared with decision tree (DT) which is a machine learning based model. Both the algorithms were optimized using hyperparameter tuning. The best performance was obtained in the case of MLP with an accuracy of 90.16% which was shown on 250 maximum iterations and each of the two hidden layers consisting of 16 number of nodes or neurons.

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Heart Disease Prediction Using Hyperparameter Tuning of Multilayer Perceptron and Decision Tree

  • 会议论文

  • 发表日期：

    2025年

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  • Priyanka Gupta,
  • R. P. Mahapatra

摘要

Heart Diseases (HD) lead to a high rate of mortality, so are considered major health problem in the population. Therefore, early diagnosis of these diseases is very important. This paper proposes deep learning-based model MLP (Multilayer Perceptron) for predicting heart or cardiac disease. The performance of MLP was compared with decision tree (DT) which is a machine learning based model. Both the algorithms were optimized using hyperparameter tuning. The best performance was obtained in the case of MLP with an accuracy of 90.16% which was shown on 250 maximum iterations and each of the two hidden layers consisting of 16 number of nodes or neurons.