Volume 34, Issue 7 (10-2026)
J Shahid Sadoughi Univ Med Sci Health Serv. 2026, 34(7): 10451-10463 |
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Ethics code: IR.SSU.REC.1401.016
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Barzegari A, Abdulmaleki M, Mirzaei M. Prediction of Ischemic Heart Disease with A Hybrid Algorithm of Support Vector Machine (SVM) and Xgboost Using Data from Yazd Health Study (YaHS). J Shahid Sadoughi Univ Med Sci Health Serv. 2026; 34 (7) :10451-10463
URL: http://jssu.ssu.ac.ir/article-1-6562-en.html
URL: http://jssu.ssu.ac.ir/article-1-6562-en.html
Abstract: (237 Views)
Introduction: Ischemic heart disease represents one of the most prevalent conditions characterized by elevated mortality rates. The aim of this research was to provide an efficient instrument for the early diagnosis of ischemic heart disease and to assist clinical decision-making processes through the application of hybrid artificial intelligence algorithms.
Methods: The data used in this study were from Yazd Health Study (YaHS) enrollment phase dataset, comprising 9969 participants and 30 selective features from a total of 300, collected between 2014 to 2015. Data were analyzed using SVM and XGBoost machine learning algorithms using Python version 3.11.8.
Results: Combining the strengths of SVM and XGBoost models through a Voting Classifier approach enhanced both the accuracy and efficiency of the heart disease prediction model. When evaluating the predictive performance for heart disease, the Voting Classifier model achieved in accuracy rate of 90%.
Conclusion: These findings suggest that implementing machine learning techniques and hybrid algorithms for early diagnosis and prediction could facilitate earlier intervention in CHD, ultimately helping to lower mortality rates.
Methods: The data used in this study were from Yazd Health Study (YaHS) enrollment phase dataset, comprising 9969 participants and 30 selective features from a total of 300, collected between 2014 to 2015. Data were analyzed using SVM and XGBoost machine learning algorithms using Python version 3.11.8.
Results: Combining the strengths of SVM and XGBoost models through a Voting Classifier approach enhanced both the accuracy and efficiency of the heart disease prediction model. When evaluating the predictive performance for heart disease, the Voting Classifier model achieved in accuracy rate of 90%.
Conclusion: These findings suggest that implementing machine learning techniques and hybrid algorithms for early diagnosis and prediction could facilitate earlier intervention in CHD, ultimately helping to lower mortality rates.
Type of Study: Original article |
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Received: 2025/11/8 | Accepted: 2026/05/6 | Published: 2026/10/7
Received: 2025/11/8 | Accepted: 2026/05/6 | Published: 2026/10/7
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