{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T09:40:43Z","timestamp":1760348443205,"version":"build-2065373602"},"reference-count":94,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:00:00Z","timestamp":1760140800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Cardiovascular disease (CVD) is a major cause of mortality around the world. This underscores the critical need to implement effective predictive tools to inform clinical decision-making. This study aimed to compare the predictive performance of ensemble learning algorithms, including Bagging, Random Forest, Extra Trees, Gradient Boosting, and AdaBoost, when applied to a clinical dataset comprising patients with CVD. The methodology entailed data preprocessing and cross-validation to regulate generalization. The performance of the model was evaluated using a variety of metrics, including accuracy, F1 score, precision, recall, Cohen\u2019s Kappa, and area under the curve (AUC). Among the models evaluated, Bagging demonstrated the best overall performance (accuracy \u00b1 SD: 93.36% \u00b1 0.22; F1 score: 0.936; AUC: 0.9686). It also reached the lowest average rank (1.0) in Friedman test and was placed, together with Extra Trees (accuracy \u00b1 SD: 90.76% \u00b1 0.18; F1 score: 0.916; AUC: 0.9689), in the superior statistical group (group A) according to Nemenyi post hoc test. The two models demonstrated a high degree of agreement with the actual labels (Kappa: 0.87 and 0.83, respectively), thereby substantiating their reliability in authentic clinical contexts. The findings substantiated the preeminence of aggregation-based ensemble methods in terms of accuracy, stability, and concordance. This underscored the prominence of Bagging and Extra Trees as optimal candidates for cardiovascular diagnostic support systems, where reliability and generalization were paramount.<\/jats:p>","DOI":"10.3390\/informatics12040109","type":"journal-article","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T08:10:31Z","timestamp":1760343031000},"page":"109","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparison of Ensemble and Meta-Ensemble Models for Early Risk Prediction of Acute Myocardial Infarction"],"prefix":"10.3390","volume":"12","author":[{"given":"Daniel Crist\u00f3bal","family":"Andrade-Gir\u00f3n","sequence":"first","affiliation":[{"name":"Department of Formal and Natural Sciences, Universidad Nacional Jos\u00e9 Faustino S\u00e1nchez Carri\u00f3n, Lima 15136, Peru"}]},{"given":"Juana","family":"Sandivar-Rosas","sequence":"additional","affiliation":[{"name":"Department of Engineering, Universidad Nacional Mayor de San Marcos, Lima 15081, Peru"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0861-9663","authenticated-orcid":false,"given":"William Joel","family":"Marin-Rodriguez","sequence":"additional","affiliation":[{"name":"Department of Engineering Systems, Computer and Electronics, Universidad Nacional Jos\u00e9 Faustino S\u00e1nchez Carri\u00f3n, Lima 15136, Peru"}]},{"given":"Marcelo Gumercindo","family":"Z\u00fa\u00f1iga-Rojas","sequence":"additional","affiliation":[{"name":"Department of Sociology, Universidad Nacional Jos\u00e9 Faustino S\u00e1nchez Carri\u00f3n, Lima 15136, Peru"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2799-3244","authenticated-orcid":false,"given":"Abrah\u00e1n Cesar","family":"Neri-Ayala","sequence":"additional","affiliation":[{"name":"Department of Administration and Management, Universidad Nacional Jos\u00e9 Faustino S\u00e1nchez Carri\u00f3n, Lima 15136, Peru"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2841-7014","authenticated-orcid":false,"given":"Ernesto","family":"D\u00edaz-Ronceros","sequence":"additional","affiliation":[{"name":"Department of Engineering Systems, Computer and Electronics, Universidad Nacional Jos\u00e9 Faustino S\u00e1nchez Carri\u00f3n, Lima 15136, Peru"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sun, J., Qiao, Y., Zhao, M., Magnussen, C.G., and Xi, B. (2023). Global, Regional, and National Burden of Cardiovascular Diseases in Youths and Young Adults Aged 15\u201339 Years in 204 Countries\/Territories, 1990\u20132019: A Systematic Analysis of Global Burden of Disease Study 2019. BMC Med., 21.","DOI":"10.1186\/s12916-023-02925-4"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e1332","DOI":"10.1016\/S2214-109X(19)30318-3","article-title":"World Health Organization Cardiovascular Disease Risk Charts: Revised Models to Estimate Risk in 21 Global Regions","volume":"7","author":"Kaptoge","year":"2019","journal-title":"Lancet Glob. Health"},{"key":"ref_3","first-page":"1","article-title":"Cardiovascular Disease as a Leading Cause of Death: How Are Pharmacists Getting Involved?","volume":"8","author":"Alzubaidi","year":"2019","journal-title":"Integr. Pharm. Res. Pract."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1002\/msj.21345","article-title":"Recognizing Global Burden of Cardiovascular Disease and Related Chronic Diseases","volume":"79","author":"Kelly","year":"2012","journal-title":"Mt. Sinai J. Med. A J. Transl. Pers. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1111\/jch.13162","article-title":"Fixed-Dose Combination Therapy to Reduce the Growing Burden of Cardiovascular Disease in Low- and Middle-Income Countries: Feasibility and Challenges","volume":"20","author":"Nansseu","year":"2018","journal-title":"J. Clin. Hypertens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1007\/s13300-024-01615-5","article-title":"Modern Management of Cardiometabolic Continuum: From Overweight\/Obesity to Prediabetes\/Type 2 Diabetes Mellitus. Recommendations from the Eastern and Southern Europe Diabetes and Obesity Expert Group","volume":"15","author":"Janez","year":"2024","journal-title":"Diabetes Ther."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1007\/s11892-019-1161-2","article-title":"Global Updates on Cardiovascular Disease Mortality Trends and Attribution of Traditional Risk Factors","volume":"19","author":"Jagannathan","year":"2019","journal-title":"Curr. Diab. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"210410","DOI":"10.1109\/ACCESS.2020.3040166","article-title":"Explainable Prediction of Acute Myocardial Infarction Using Machine Learning and Shapley Values","volume":"8","author":"Ibrahim","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Writing Committee, Smith, S.C., Collins, A., Ferrari, R., Holmes, D.R., Logstrup, S., McGhie, D.V., Ralston, J., Sacco, R.L., and Stam, H. (2012). Our Time: A Call to Save Preventable Death from Cardiovascular Disease (Heart Disease and Stroke). Eur. Heart J., 33, 2910\u20132916.","DOI":"10.1093\/eurheartj\/ehs313"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gheorghe, A., Griffiths, U., Murphy, A., Legido-Quigley, H., Lamptey, P., and Perel, P. (2018). The Economic Burden of Cardiovascular Disease and Hypertension in Low- and Middle-Income Countries: A Systematic Review. BMC Public Health, 18.","DOI":"10.1186\/s12889-018-5806-x"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Stanley, S. (2025). Family Caregivers and Cardiovascular Disease: An Intersectional Approach to Good Health and Wellbeing. International Perspectives on Family Caregiving, Emerald Publishing Limited.","DOI":"10.1108\/9781835496121"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"S1","DOI":"10.1016\/j.jacc.2012.11.002","article-title":"The Worldwide Environment of Cardiovascular Disease: Prevalence, Diagnosis, Therapy, and Policy Issues","volume":"60","author":"Laslett","year":"2012","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Capotosto, L., Massoni, F., De Sio, S., Ricci, S., and Vitarelli, A. (2018). Early Diagnosis of Cardiovascular Diseases in Workers: Role of Standard and Advanced Echocardiography. BioMed Res. Int., 2018.","DOI":"10.1155\/2018\/7354691"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s11936-006-0025-7","article-title":"Cardiovascular Disease: Optimal Approaches to Risk Factor Modification of Diet and Lifestyle","volume":"8","author":"Forman","year":"2006","journal-title":"Curr. Treat. Options Cardio Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1080\/00207411.1980.11448851","article-title":"Behavioral Approaches to Preventing Heart Disease: Risk Factor Modification","volume":"9","author":"Hymowitz","year":"1980","journal-title":"Int. J. Ment. Health"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"101922","DOI":"10.1016\/j.cpcardiol.2023.101922","article-title":"Smart Technologies Used as Smart Tools in the Management of Cardiovascular Disease and Their Future Perspective","volume":"48","author":"Ullah","year":"2023","journal-title":"Curr. Probl. Cardiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1186\/s43044-022-00317-2","article-title":"Emerging Biomarkers for the Detection of Cardiovascular Diseases","volume":"74","author":"Thupakula","year":"2022","journal-title":"Egypt Heart J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.bios.2015.03.037","article-title":"Diagnostics on Acute Myocardial Infarction: Cardiac Troponin Biomarkers","volume":"70","author":"Fathil","year":"2015","journal-title":"Biosens. Bioelectron."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/s40291-012-0011-6","article-title":"Cardiac Troponins I and T: Molecular Markers for Early Diagnosis, Prognosis, and Accurate Triaging of Patients with Acute Myocardial Infarction","volume":"16","author":"Tiwari","year":"2012","journal-title":"Mol. Diagn. Ther."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s11739-017-1612-1","article-title":"Cardiac Biomarkers of Acute Coronary Syndrome: From History to High-Sensitivity Cardiac Troponin","volume":"12","author":"Garg","year":"2017","journal-title":"Intern. Emerg. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"100748","DOI":"10.1016\/j.ijoes.2024.100748","article-title":"Advances in Electrochemical Detection of B-Type Natriuretic Peptide as a Heart Failure Biomarker","volume":"19","author":"Li","year":"2024","journal-title":"Int. J. Electrochem. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s10549-012-2039-z","article-title":"High-Sensitivity C-Reactive Protein (Hs-CRP) as a Biomarker for Trastuzumab-Induced Cardiotoxicity in HER2-Positive Early-Stage Breast Cancer: A Pilot Study","volume":"134","author":"Onitilo","year":"2012","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"971453","DOI":"10.1155\/2015\/971453","article-title":"Emerging Risk Biomarkers in Cardiovascular Diseases and Disorders","volume":"2015","author":"Upadhyay","year":"2015","journal-title":"J. Lipids"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1186\/s12944-022-01747-2","article-title":"Long-Term Prognostic Value of LDL-C, HDL-C, Lp(a) and TG Levels on Cardiovascular Disease Incidence, by Body Weight Status, Dietary Habits and Lipid-Lowering Treatment: The ATTICA Epidemiological Cohort Study (2002\u20132012)","volume":"21","author":"Georgoulis","year":"2022","journal-title":"Lipids Health Dis."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s12944-015-0031-4","article-title":"The Role of Plasma Triglyceride\/High-Density Lipoprotein Cholesterol Ratio to Predict Cardiovascular Outcomes in Chronic Kidney Disease","volume":"14","author":"Sonmez","year":"2015","journal-title":"Lipids Health Dis."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1007\/s00125-008-1062-4","article-title":"Non-Invasive Cardiac Imaging Techniques and Vascular Tools for the Assessment of Cardiovascular Disease in Type 2 Diabetes Mellitus","volume":"51","author":"Djaberi","year":"2008","journal-title":"Diabetologia"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1109\/RBME.2017.2757953","article-title":"A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records","volume":"10","author":"Ansari","year":"2017","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1007\/s12574-018-0405-5","article-title":"Echocardiographic Assessment of Left Ventricular Systolic Function","volume":"17","author":"Klaeboe","year":"2019","journal-title":"J. Echocardiogr."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Cheng, K., Lin, A., Yuvaraj, J., Nicholls, S.J., and Wong, D.T.L. (2021). Cardiac Computed Tomography Radiomics for the Non-Invasive Assessment of Coronary Inflammation. Cells, 10.","DOI":"10.3390\/cells10040879"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.jcct.2019.09.002","article-title":"State-of-the-Art-Myocardial Perfusion Stress Testing: Static CT Perfusion","volume":"14","author":"Mushtaq","year":"2020","journal-title":"J. Cardiovasc. Comput. Tomogr."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1007\/s12265-011-9290-2","article-title":"SPECT Imaging for Detecting Coronary Artery Disease and Determining Prognosis by Noninvasive Assessment of Myocardial Perfusion and Myocardial Viability","volume":"4","author":"Beller","year":"2011","journal-title":"J. Cardiovasc. Trans. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1186\/s40537-023-00817-1","article-title":"Advanced Machine Learning Techniques for Cardiovascular Disease Early Detection and Diagnosis","volume":"10","author":"Baghdadi","year":"2023","journal-title":"J. Big Data"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"100319","DOI":"10.1016\/j.health.2024.100319","article-title":"A Predictive Approach for Myocardial Infarction Risk Assessment Using Machine Learning and Big Clinical Data","volume":"5","author":"Boudali","year":"2024","journal-title":"Healthc. Anal."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Dimopoulos, A.C., Nikolaidou, M., Caballero, F.F., Engchuan, W., Sanchez-Niubo, A., Arndt, H., Ayuso-Mateos, J.L., Haro, J.M., Chatterji, S., and Georgousopoulou, E.N. (2018). Machine Learning Methodologies versus Cardiovascular Risk Scores, in Predicting Disease Risk. BMC Med. Res. Methodol., 18.","DOI":"10.1186\/s12874-018-0644-1"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s13198-022-01681-7","article-title":"A Machine Intelligence Technique for Predicting Cardiovascular Disease (CVD) Using Radiology Dataset","volume":"15","author":"Saikumar","year":"2024","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hakim, M.A., Jahan, N., Zerin, Z.A., and Farha, A.B. (2021, January 6\u20138). Performance Evaluation and Comparison of Ensemble Based Bagging and Boosting Machine Learning Methods for Automated Early Prediction of Myocardial Infarction. Proceedings of the 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India.","DOI":"10.1109\/ICCCNT51525.2021.9580063"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5366","DOI":"10.1007\/s10489-021-02696-6","article-title":"Hybrid CNN-LSTM Deep Learning Model and Ensemble Technique for Automatic Detection of Myocardial Infarction Using Big ECG Data","volume":"52","author":"Rai","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_38","first-page":"4209","article-title":"Machine Learning Optimization Techniques: A Survey, Classification, Challenges, and Future Research Issues","volume":"31","author":"Bian","year":"2024","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Simon, G.J., and Aliferis, C. (2024). Overfitting, Underfitting and General Model Overconfidence and Under-Performance Pitfalls and Best Practices in Machine Learning and AI. Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfalls, Springer International Publishing.","DOI":"10.1007\/978-3-031-39355-6_10"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e47645","DOI":"10.2196\/47645","article-title":"Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions","volume":"26","author":"Cai","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"105837","DOI":"10.1016\/j.asoc.2019.105837","article-title":"Ensemble Approach Based on Bagging, Boosting and Stacking for Short-Term Prediction in Agribusiness Time Series","volume":"86","author":"Ribeiro","year":"2020","journal-title":"Appl. Soft. Comput."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Krittanawong, C., Virk, H.U.H., Bangalore, S., Wang, Z., Johnson, K.W., Pinotti, R., Zhang, H., Kaplin, S., Narasimhan, B., and Kitai, T. (2020). Machine Learning Prediction in Cardiovascular Diseases: A Meta-Analysis. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-72685-1"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Liu, R., Wang, M., Zheng, T., Zhang, R., Li, N., Chen, Z., Yan, H., and Shi, Q. (2022). An Artificial Intelligence-Based Risk Prediction Model of Myocardial Infarction. BMC Bioinform., 23.","DOI":"10.1186\/s12859-022-04761-4"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wang, S., Li, J., Sun, L., Cai, J., Wang, S., Zeng, L., and Sun, S. (2021). Application of Machine Learning to Predict the Occurrence of Arrhythmia after Acute Myocardial Infarction. BMC Med. Inf. Decis. Mak., 21.","DOI":"10.1186\/s12911-021-01667-8"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1007\/s11760-017-1146-z","article-title":"Inferior Myocardial Infarction Detection Using Stationary Wavelet Transform and Machine Learning Approach","volume":"12","author":"Sharma","year":"2018","journal-title":"Signal Image Video Process."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Oliveira, M., Seringa, J., Pinto, F.J., Henriques, R., and Magalh\u00e3es, T. (2023). Machine Learning Prediction of Mortality in Acute Myocardial Infarction. BMC Med. Inf. Decis. Mak., 23.","DOI":"10.1186\/s12911-023-02168-6"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Li, X., Shang, C., Xu, C., Wang, Y., Xu, J., and Zhou, Q. (2023). Development and Comparison of Machine Learning-Based Models for Predicting Heart Failure after Acute Myocardial Infarction. BMC Med. Inf. Decis. Mak., 23.","DOI":"10.1186\/s12911-023-02240-1"},{"key":"ref_48","first-page":"1207","article-title":"Machine Learning Compared with Conventional Statistical Models for Predicting Myocardial Infarction Readmission and Mortality: A Systematic Review","volume":"37","author":"Cho","year":"2021","journal-title":"Can. J. Cardiol."},{"key":"ref_49","first-page":"1777","article-title":"Machine Learning in Sudden Cardiac Death Risk Prediction: A Systematic Review","volume":"24","author":"Barker","year":"2022","journal-title":"EP Eur."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chellappan, D., and Rajaguru, H. (2025). Generalizability of Machine Learning Models for Diabetes Detection a Study with Nordic Islet Transplant and PIMA Datasets. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-87471-0"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.1007\/s11053-024-10396-4","article-title":"Interpretable SHAP Model Combining Meta-Learning and Vision Transformer for Lithology Classification Using Limited and Unbalanced Drilling Data in Well Logging","volume":"33","author":"Sun","year":"2024","journal-title":"Nat. Resour. Res."},{"key":"ref_52","first-page":"135","article-title":"Diagnostic Strategies Using AI and ML in Cardiovascular Diseases: Challenges and Future Perspectives","volume":"Volume 1","author":"Dulhare","year":"2025","journal-title":"Deep Learning and Computer Vision: Models and Biomedical Applications"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.neucom.2020.03.064","article-title":"AdaBoost-CNN: An Adaptive Boosting Algorithm for Convolutional Neural Networks to Classify Multi-Class Imbalanced Datasets Using Transfer Learning","volume":"404","author":"Taherkhani","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"745","DOI":"10.3724\/SP.J.1004.2013.00745","article-title":"Advance and Prospects of AdaBoost Algorithm","volume":"39","author":"Cao","year":"2013","journal-title":"Acta Autom. Sin."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"103770","DOI":"10.1016\/j.engappai.2020.103770","article-title":"Boosting Algorithms for Network Intrusion Detection: A Comparative Evaluation of Real AdaBoost, Gentle AdaBoost and Modest AdaBoost","volume":"94","author":"Shahraki","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1007\/s10462-020-09896-5","article-title":"A Comparative Analysis of Gradient Boosting Algorithms","volume":"54","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Singh Tomar, G., Chaudhari, N.S., Barbosa, J.L.V., and Aghwariya, M.K. (2020). Study of AdaBoost and Gradient Boosting Algorithms for Predictive Analytics. Proceedings of the International Conference on Intelligent Computing and Smart Communication, Tehri, India, 20\u201321 April 2019, Springer.","DOI":"10.1007\/978-981-15-0633-8"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"105942","DOI":"10.1016\/j.asoc.2019.105942","article-title":"A Gradient Boosting Decision Tree Based GPS Signal Reception Classification Algorithm","volume":"86","author":"Sun","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Aziz, N., Akhir, E.A.P., Aziz, I.A., Jaafar, J., Hasan, M.H., and Abas, A.N.C. (2020, January 8\u20139). A Study on Gradient Boosting Algorithms for Development of AI Monitoring and Prediction Systems. Proceedings of the 2020 International Conference on Computational Intelligence (ICCI), Virtual.","DOI":"10.1109\/ICCI51257.2020.9247843"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s11517-018-1878-0","article-title":"A Random Forest Classifier-Based Approach in the Detection of Abnormalities in the Retina","volume":"57","author":"Chowdhury","year":"2019","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Dhananjay, B., Venkatesh, N.P., Bhardwaj, A., and Sivaraman, J. (2021, January 26\u201327). Cardiac Signals Classification Based on Extra Trees Model. Proceedings of the 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India.","DOI":"10.1109\/SPIN52536.2021.9565992"},{"key":"ref_62","first-page":"100094","article-title":"A Comparison among Interpretative Proposals for Random Forests","volume":"6","author":"Aria","year":"2021","journal-title":"Mach. Learn. Appl."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1109\/TPAMI.2008.30","article-title":"A Theoretical Analysis of Bagging as a Linear Combination of Classifiers","volume":"30","author":"Fumera","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1007\/s00357-021-09397-2","article-title":"Comparing Boosting and Bagging for Decision Trees of Rankings","volume":"39","author":"Plaia","year":"2022","journal-title":"J. Classif."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"19083","DOI":"10.1109\/ACCESS.2022.3151048","article-title":"MLCM: Multi-Label Confusion Matrix","volume":"10","author":"Heydarian","year":"2022","journal-title":"IEEE Access"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Markoulidakis, I., and Markoulidakis, G. (2024). Probabilistic Confusion Matrix: A Novel Method for Machine Learning Algorithm Generalized Performance Analysis. Technologies, 12.","DOI":"10.3390\/technologies12070113"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1007\/s10559-022-00460-3","article-title":"Justification for the Use of Cohen\u2019s Kappa Statistic in Experimental Studies of NLP and Text Mining","volume":"58","author":"Kolesnyk","year":"2022","journal-title":"Cybern. Syst. Anal."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"164386","DOI":"10.1109\/ACCESS.2019.2953104","article-title":"A Simplified Cohen\u2019s Kappa for Use in Binary Classification Data Annotation Tasks","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_69","first-page":"1233","article-title":"A Fuzzy Classification Model for Myocardial Infarction Risk Assessment","volume":"48","author":"Mokeddem","year":"2018","journal-title":"Appl. Intell."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"e1557","DOI":"10.1002\/ecm.1557","article-title":"Cross Validation for Model Selection: A Review with Examples from Ecology","volume":"93","author":"Yates","year":"2023","journal-title":"Ecol. Monogr."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1080\/10618600.2015.1020159","article-title":"Estimation Stability with Cross-Validation (ESCV)","volume":"25","author":"Lim","year":"2016","journal-title":"J. Comput. Graph. Stat."},{"key":"ref_72","unstructured":"Raschka, S. (2020). Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning. arXiv."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"2527","DOI":"10.1007\/s11517-023-02841-y","article-title":"A Biomarker Discovery of Acute Myocardial Infarction Using Feature Selection and Machine Learning","volume":"61","author":"Hon","year":"2023","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Obuchowski, N.A., and Bullen, J.A. (2018). Receiver Operating Characteristic (ROC) Curves: Review of Methods with Applications in Diagnostic Medicine. Phys. Med. Biol., 63.","DOI":"10.1088\/1361-6560\/aab4b1"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1007\/s00134-025-07848-7","article-title":"A Common Longitudinal Intensive Care Unit Data Format (CLIF) for Critical Illness Research","volume":"51","author":"Rojas","year":"2025","journal-title":"Intensive Care Med."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.ins.2019.07.070","article-title":"A Comprehensive Analysis of Synthetic Minority Oversampling Technique (SMOTE) for Handling Class Imbalance","volume":"505","author":"Elreedy","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"114986","DOI":"10.1016\/j.eswa.2021.114986","article-title":"Classification of Imbalanced Hyperspectral Images Using SMOTE-Based Deep Learning Methods","volume":"178","author":"Polat","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Carreira-Perpi\u00f1\u00e1n, M.\u00c1., and Zharmagambetov, A. (2020). Ensembles of Bagged TAO Trees Consistently Improve over Random Forests, AdaBoost and Gradient Boosting. Proceedings of the 2020 ACM-IMS on Foundations of Data Science Conference, Virtual, 18\u201320 October 2020, Association for Computing Machinery.","DOI":"10.1145\/3412815.3416882"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"2487","DOI":"10.1007\/s10980-019-00916-6","article-title":"Overselling Overall Map Accuracy Misinforms about Research Reliability","volume":"34","author":"Shao","year":"2019","journal-title":"Landsc. Ecol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1186\/s13071-022-05402-8","article-title":"Methodological Issues on Evaluating Agreement between Two Detection Methods by Cohen\u2019s Kappa Analysis. Parasit","volume":"15","author":"Li","year":"2022","journal-title":"Vectors"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Demirhan, H., and Yilmaz, A.E. (2023). Detection of Grey Zones in Inter-Rater Agreement Studies. BMC Med. Res. Methodol., 23.","DOI":"10.1186\/s12874-022-01759-7"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1007\/s10115-017-1022-8","article-title":"Prequential AUC: Properties of the Area under the ROC Curve for Data Streams with Concept Drift","volume":"52","author":"Brzezinski","year":"2017","journal-title":"Knowl. Inf. Syst."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"101361","DOI":"10.1016\/j.imu.2023.101361","article-title":"Predicting Complications of Myocardial Infarction within Several Hours of Hospitalization Using Data Mining Techniques","volume":"42","author":"Newaz","year":"2023","journal-title":"Inform. Med. Unlocked"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"76003","DOI":"10.1109\/ACCESS.2024.3401744","article-title":"A Novel Deep Learning Approach for Myocardial Infarction Detection and Multi-Label Classification","volume":"12","author":"Abbas","year":"2024","journal-title":"IEEE Access"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1007\/s10586-024-04964-9","article-title":"Intrusion Detection in Smart Grids Using Artificial Intelligence-Based Ensemble Modelling","volume":"28","author":"Alsirhani","year":"2025","journal-title":"Clust. Comput."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1016\/j.jclinepi.2007.03.015","article-title":"The Evaluation of Diagnostic Tests: Evidence on Technical and Diagnostic Accuracy, Impact on Patient Outcome and Cost-Effectiveness Is Needed","volume":"60","author":"Cleemput","year":"2007","journal-title":"J. Clin. Epidemiol."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1007\/s12065-021-00565-2","article-title":"Precision\u2013Recall Curve (PRC) Classification Trees","volume":"15","author":"Miao","year":"2022","journal-title":"Evol. Intel."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1080\/19401493.2018.1498538","article-title":"Advanced Machine Learning Techniques for Building Performance Simulation: A Comparative Analysis","volume":"12","author":"Chakraborty","year":"2019","journal-title":"J. Build. Perform. Simul."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1080\/01621459.2016.1273116","article-title":"Boosting in the Presence of Outliers: Adaptive Classification with Nonconvex Loss Functions","volume":"113","author":"Li","year":"2018","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"78368","DOI":"10.1109\/ACCESS.2021.3084050","article-title":"The Matthews Correlation Coefficient (MCC) Is More Informative Than Cohen\u2019s Kappa and Brier Score in Binary Classification Assessment","volume":"9","author":"Chicco","year":"2021","journal-title":"IEEE Access"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Wallace, M.L., Mentch, L., Wheeler, B.J., Tapia, A.L., Richards, M., Zhou, S., Yi, L., Redline, S., and Buysse, D.J. (2023). Use and Misuse of Random Forest Variable Importance Metrics in Medicine: Demonstrations through Incident Stroke Prediction. BMC Med. Res. Methodol., 23.","DOI":"10.1186\/s12874-023-01965-x"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/10408363.2023.2235426","article-title":"Establishing Optimal Cutoff Values for High-Sensitivity Cardiac Troponin Algorithms in Risk Stratification of Acute Myocardial Infarction","volume":"61","author":"Liu","year":"2024","journal-title":"Crit. Rev. Clin. Lab. Sci."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"113692","DOI":"10.1109\/ACCESS.2021.3099795","article-title":"A Stacking Ensemble Prediction Model for the Occurrences of Major Adverse Cardiovascular Events in Patients with Acute Coronary Syndrome on Imbalanced Data","volume":"9","author":"Zheng","year":"2021","journal-title":"IEEE Access"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Kasim, S., Amir Rudin, P.N.F., Malek, S., Ibrahim, K.S., Wan Ahmad, W.A., Fong, A.Y.Y., Lin, W.Y., Aziz, F., and Ibrahim, N. (2024). Ensemble Machine Learning for Predicting In-Hospital Mortality in Asian Women with ST-Elevation Myocardial Infarction (STEMI). Sci. Rep., 14.","DOI":"10.1038\/s41598-024-61151-x"}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/12\/4\/109\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T09:04:00Z","timestamp":1760346240000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/12\/4\/109"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,11]]},"references-count":94,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["informatics12040109"],"URL":"https:\/\/doi.org\/10.3390\/informatics12040109","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,11]]}}}