Improving Heart Disease Prediction Accuracy through Machine Learning Algorithms

  • Hussam Elbehiery Department of Artificial Intelligence, Faculty of Information Systems and Computer Science, 6 October University, Cairo, Egypt
  • Moshira A. Ebrahim Department of Computer Engineering and Information Technology, Modern Academy of Engineering and Technology, Cairo, Egypt
  • Mohamed Eassa Department of Artificial Intelligence, Faculty of Information Systems and Computer Science, 6 October University, Cairo, Egypt; Applied Science Research Centre, Applied Science Private University, Amman, Jordan
  • Ahmed Abdelhafeez Department of Artificial Intelligence, Faculty of Information Systems and Computer Science, 6 October University, Cairo, Egypt; Applied Science Research Centre, Applied Science Private University, Amman, Jordan
  • Aya Omar Department of Artificial Intelligence, Faculty of Information Systems and Computer Science, 6 October University, Cairo, Egypt
  • Hadeer Mahmoud Department of Artificial Intelligence, Faculty of Computers and Artificial Intelligence, Modern University for Technology & Information, Cairo, Egypt
DOI: https://doi.org/10.19139/soic-2310-5070-2319
Keywords: Data Visualization, data processing, Random Forest Algorithm, Logistic regression Algorithm, Gradient Boosting Algorithm, Ada Boost Algorithm, support vector machine, and XGBoost.

Abstract

This study explores the application of a range of machine learning and deep learning techniques for predicting cardiovascular diseases. Various models, including Random Forest, Logistic Regression, Gradient Boosting, AdaBoost, Support Vector Machine (SVM), XGBoost, and both Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks are evaluated. A comprehensive evaluation is conducted by considering supplementary metrics, refining hyperparameter tuning, assessing feature importance using SHAP, comparing traditional machine learning with deep learning approaches, and examining the clinical relevance. It concludes that XGBoost achieves the highest accuracy (88%), and notes that CNN and LSTM may prove beneficial with larger datasets. Moreover, the study investigates the practical applications of these models, focusing on their potential integration into clinical decision support systems.
Published
2025-05-14
How to Cite
Hussam Elbehiery, Moshira A. Ebrahim, Mohamed Eassa, Ahmed Abdelhafeez, Aya Omar, & Mahmoud, H. (2025). Improving Heart Disease Prediction Accuracy through Machine Learning Algorithms. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-2319
Issue
Online First
Section
Research Articles
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