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Mansoor, H., Elgendy, I.Y., Segal, R., Bavry, A.A. and Bian, J. (2017) Risk Prediction Model for In-Hospital Mortality in Women with ST-Elevation Myocardial Infarction: A Machine Learning Approach. Heart & Lung, 46, 405-411.
https://doi.org/10.1016/j.hrtlng.2017.09.003

has been cited by the following article:

• TITLE: Machine Learning-Based Classification of Coronary Heart Disease: A Comparative Analysis of Logistic Regression, Random Forest, and Support Vector Machine Models

  AUTHORS: Zakia Sultana Munmun, Salma Akter, Chowdhury Raihan Parvez

  KEYWORDS: Coronary Heart Disease, Machine Learning, Classification, Random Forest, Support Vector Machine, Logistic Regression

  JOURNAL NAME: Open Access Library Journal, Vol.12 No.3, March 26, 2025

  ABSTRACT: The accurate and early detection of coronary heart disease (CHD) is crucial for reducing mortality rates. This study evaluates the predictive performance of three machine learning models—Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM)—for classifying CHD. The models were trained on a comprehensive heart disease dataset and assessed using metrics such as Accuracy, Specificity, Sensitivity, F1-score, Negative Predictive Value, and Positive Predictive Value. Among the models, RF demonstrated the highest accuracy (93.5%), while SVM excelled in sensitivity (97.5%). The findings highlight the potential of machine learning techniques in clinical decision-making and personalized medicine.