Machine Learning-Based Classification of Coronary Heart Disease: A Comparative Analysis of Logistic Regression, Random Forest, and Support Vector Machine Models
Year: 2025
Type: article
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.
Source: OALib
Institutions: Central Michigan University, Bangladesh Medical University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4408841731