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August 15, 2025Journal of Al-Qadisiyah for Computer Science and MathematicsOpen Access

Performance Comparison of Machine Learning Algorithms in Heart Disease Prediction with Enhanced Accuracy through Hyper parameter Tuning

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Authors

NHNisreen Ryadh HamzaUniversity of Al-QadisiyahFAFarah Jawad Al-Ghanim

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Implication

This analysis reveals improved accuracy for heart disease prediction using machine learning models, highlighting hyperparameter tuning's role.

Key Points

  • Random Forest achieved the highest accuracy at 96.30% in predicting heart disease, outperforming other models.
  • The accuracy of models included Decision Tree at 95.59%, SVM at 94.55%, KNN at 94.25%, and Naive Bayes at 69.66%.
  • Preprocessing was employed to clean data from 1,888 records and ensure optimal learning patterns for the models.
  • Hyperparameter tuning aimed to enhance the performance of both SVM and KNN models during evaluation.

Cite This Study

Hamza et al. (2025) studied this question.

synapsesocial.com/papers/68a3656a0a429f797332b93fhttps://doi.org/10.29304/jqcsm.2025.17.22180
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Also Consider

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  1. 1A comprehensive comparative analysis of machine learning algorithms in heart disease prediction2026 · 2 citations
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