{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:16:33Z","timestamp":1786115793571,"version":"build-2736575974"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med. Technol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Heart disease classification using small structured clinical datasets requires evaluation procedures that account for sampling variability, model-selection bias, probability calibration, and clinically relevant operating characteristics. This study presents an internally validated comparison of classical classifiers, modern tree ensembles, and regularized artificial neural networks using a cleaned Cleveland heart disease dataset containing 303 observations, 13 predictors, and a binary outcome.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Continuous variables were standardized, nominal variables were one-hot encoded, and all preprocessing transformations were fitted exclusively within training folds. Model performance was evaluated using five-fold outer cross-validation repeated five times, with inner stratified cross-validation for hyperparameter optimisation. Twelve models were assessed using accuracy, sensitivity, specificity, positive and negative predictive values, F1-score, Matthew's correlation coefficient, ROC-AUC, PR-AUC, Brier score, and calibration measures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>CatBoost achieved the highest mean ROC-AUC of 0.909\u2009\u00b1\u20090.041 and F1-score of 0.862\u2009\u00b1\u20090.041, while Logistic Regression achieved a comparable ROC-AUC of 0.906\u2009\u00b1\u20090.041. Random Forest obtained the highest mean PR-AUC of 0.916\u2009\u00b1\u20090.040. CatBoost did not significantly outperform Logistic Regression in ROC-AUC, PR-AUC, F1-score, or paired classification errors. Held-out permutation analysis identified the number of major vessels, thalassemia status, and chest-pain category as the most influential predictors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The findings indicate that no model family was universally superior and that a carefully regularized linear model remained competitive with substantially more complex approaches. These findings represent internal validation only and require confirmation using independent clinical cohorts before deployment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmedt.2026.1902471","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T05:33:14Z","timestamp":1785994394000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A comparative performance analysis of ensemble learning and regularized neural networks in cardiovascular risk prediction"],"prefix":"10.3389","volume":"8","author":[{"given":"Golam","family":"Md Mohiuddin","sequence":"first","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University","place":["Bukit Beruang, Melaka, Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Shakil","family":"Hossain","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, Manarat International University","place":["Dhaka, Bangladesh"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Shohel","family":"Sayeed","sequence":"additional","affiliation":[{"name":"Centre for Intelligent Cloud Computing, CoE for Advanced Cloud, Faculty of Information Science and Technology, Multimedia University","place":["Bukit Beruang, Melaka, Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,6]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"24568","DOI":"10.1038\/s41598-025-09423-y","article-title":"Effectiveness of machine learning models in diagnosis of heart disease: a comparative study","volume":"15","author":"Alsabhan","year":"2025","journal-title":"Sci Rep"},{"key":"B2","doi-asserted-by":"publisher","first-page":"875","DOI":"10.32604\/cmes.2025.061623","article-title":"Heart disease prediction model using feature selection and ensemble deep learning with optimized weight","volume":"143","author":"Al-Mahdi","year":"2025","journal-title":"Comput Model Eng Sci"},{"key":"B3","doi-asserted-by":"publisher","first-page":"e0174944","DOI":"10.1371\/journal.pone.0174944","article-title":"Can machine-learning improve cardiovascular risk prediction using routine clinical data?","volume":"12","author":"Weng","year":"2017","journal-title":"PLoS One"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.3390\/s23031161","article-title":"Efficient data-driven machine learning models for cardiovascular diseases risk prediction","volume":"23","author":"Dritsas","year":"2023","journal-title":"Sensors"},{"key":"B5","doi-asserted-by":"publisher","first-page":"565","DOI":"10.70917\/ijcisim-2025-0036","article-title":"Comparative analysis of computational intelligence techniques for predicting coronary artery heart disease: a study on logistic regression, support vector machine, and K-nearest neighbor","volume":"17","author":"Dhanka","year":"2025","journal-title":"Int J Comput Inf Syst Ind Manag Appl."},{"key":"B6","doi-asserted-by":"publisher","first-page":"427","DOI":"10.3390\/biomedicines13020427","article-title":"The heart of transformation: exploring artificial intelligence in cardiovascular disease","volume":"13","author":"Chowdhury","year":"2025","journal-title":"Biomedicines"},{"key":"B7","first-page":"1319","article-title":"ML-based Heart disease prediction: naive Bayes and decision tree performance comparison","author":"Rathinasamy","year":"2025"},{"key":"B8","first-page":"66","article-title":"Enhancing early heart disease detection through comparative analysis of random forest, decision tree, and K-NN models","volume":"10","author":"Kohsasih","year":"2025","journal-title":"IT J Res Dev."},{"key":"B9","doi-asserted-by":"publisher","first-page":"13361","DOI":"10.1038\/s41598-025-96437-1","article-title":"Predicting coronary heart disease with advanced machine learning classifiers for improved cardiovascular risk assessment","volume":"15","author":"Rehman","year":"2025","journal-title":"Sci Rep"},{"key":"B10","doi-asserted-by":"publisher","first-page":"189503","DOI":"10.1109\/ACCESS.2020.3026214","article-title":"Enhanced deep learning assisted convolutional neural network for heart disease prediction on the internet of medical things platform","volume":"8","author":"Pan","year":"2020","journal-title":"IEEE Access"},{"key":"B11","first-page":"1271","article-title":"Hybrid optimisation algorithms for accurate prediction and classification of cardiovascular diseases","author":"Kumar","year":"2025"},{"key":"B12","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1007\/s12626-025-00190-w","article-title":"Boosting machine learning accuracy for cardiac disease prediction: the role of advanced feature engineering and model optimisation","volume":"19","author":"Bilal","year":"2025","journal-title":"Rev. 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