Comparative Evaluation of Machine Learning Algorithms for Binary Classification of Heart Disease: A Cross-Validated Analysis of a Clinical Dataset
Year: 2026
Type: article
Abstract: Machine learning (ML) algorithms are increasingly used to classify heart disease using routinely collected clinical variables. This study compared nine supervised classifiers to identify the best-performing model for binary heart disease classification. A secondary analysis was conducted using a dataset of 303 observations, 13 predictors, and a binary outcome. Data completeness and outcome distribution were assessed, and no missing values were identified. The dataset included 165 positive cases (54.5%) and 138 negative cases (45.5%). Logistic Regression, Random Forest, Extra Trees, AdaBoost, Gradient Boosting, radial-basis-function Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gaussian Naive Bayes, and Decision Tree were evaluated using five-fold stratified cross-validation. Numerical predictors were standardized, and categorical predictors were one-hot encoded within preprocessing pipelines. Performance was assessed using accuracy, precision, sensitivity, specificity, F1-score, and receiver operating characteristic area under the curve (ROC-AUC). Logistic Regression achieved the highest reported accuracy (86.5%), precision (85.2%), sensitivity (90.9%), specificity (81.2%), F1-score (88.0%), and ROC-AUC (0.914). Random Forest and AdaBoost achieved ROC-AUC values of 0.901, while Extra Trees achieved 0.896. Decision Tree performed least effectively, with an accuracy of 74.3% and ROC-AUC of 0.739. Logistic Regression emerged as the strongestperforming classifier in this comparison and represents a reasonable baseline for further investigation. However, the findings remain preliminary because confidence intervals, formal paired comparisons, nested hyperparameter tuning, calibration assessment, and independent external validation were unavailable. Further research should reproduce the analysis using the raw dataset and evaluate model generalizability before considering clinical applications.
Authors: Taniya Mondal, Durjodhan Mondal
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7222401904
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