{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T22:16:55Z","timestamp":1790115415788,"version":"4.0.1"},"reference-count":94,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T00:00:00Z","timestamp":1776384000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Reliable predictive modeling in high-dimensional biomedical data requires a balance between accuracy, interpretability, and computational efficiency. However, existing ensemble methods often overlook model diversity or rely on ad hoc feature-selection approaches, which limit generalizability. This study introduces a hybrid feature-selection and diversity-guided stacking framework designed to improve robustness and scalability across clinical and other data-intensive domains.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The proposed framework integrates a hybrid feature-selection pipeline\u2014combining Variance Inflation Factor (VIF), Analysis of Variance (ANOVA), Sequential Backward Elimination (SBE), and Lasso regression\u2014to reduce multicollinearity and overfitting. It also employs a diversity-aware stacking strategy that constructs sub-model sets based on pairwise diversity measures (Disagreement, Yule\u2019s Q, and Cohen\u2019s Kappa) and non-pairwise metrics (Entropy and Kohavi\u2013Wolpert). Sixteen base classifiers and five meta-learners were trained using repeated 10-fold cross-validation. The framework was evaluated using data from 4,778 hospitalized COVID-19 patients with 116 clinical and laboratory attributes, preprocessed using robust scaling and ROSE-based class balancing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The optimal configuration, which stacked Random Forest and XGBoost models using a Neural Network meta-learner, achieved 91.4% accuracy (95% CI: 89.8\u201392.8), AUC\u2009=\u20090.955, F1\u2009=\u20090.801, and MCC\u2009=\u20090.746, outperforming the best individual model (AdaBoost, 90.2%). Training time (~450\u2009s) and per-case inference time (&lt;0.2\u2009s) demonstrated computational feasibility. Feature-importance analysis and SHAP-based interpretation confirmed clinical relevance and interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The hybrid feature-selection and diversity-guided stacking framework improves predictive accuracy and interpretability while maintaining computational efficiency. Although validated using COVID-19 mortality data, the approach is broadly applicable to biomedical, environmental, and engineering prediction tasks that require interpretable and scalable ensemble learning.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0341198","type":"journal-article","created":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T17:28:13Z","timestamp":1776446893000},"page":"e0341198","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Hybrid feature-selection and diversity-guided stacking framework for interpretable ensemble learning: Application to COVID-19 mortality prediction"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6927-9589","authenticated-orcid":true,"given":"Farideh","family":"Mohtasham","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyed Saeed","family":"Hashemi Nazari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamad Amin","family":"Pourhoseingholi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1906-3912","authenticated-orcid":true,"given":"Kaveh","family":"Kavousi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Reza","family":"Zali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,4,17]]},"reference":[{"issue":"2","key":"pone.0341198.ref001","first-page":"1301","article-title":"A survey on machine learning: concept, algorithms and applications","volume":"5","author":"K Das","year":"2017","journal-title":"International Journal of Innovative Research in Computer and Communication Engineering"},{"issue":"2","key":"pone.0341198.ref002","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/S1566-2535(01)00029-X","article-title":"Binary labelling and decision-level fusion","volume":"2","author":"T Windeatt","year":"2001","journal-title":"Information Fusion"},{"key":"pone.0341198.ref003","doi-asserted-by":"crossref","first-page":"100242","DOI":"10.1016\/j.dajour.2023.100242","article-title":"A novel machine learning model with Stacking Ensemble Learner for predicting emergency readmission of heart-disease patients","volume":"7","author":"A Ghasemieh","year":"2023","journal-title":"Decision Analytics Journal"},{"key":"pone.0341198.ref004","doi-asserted-by":"crossref","unstructured":"Berliana AU, Bustamam A. Implementation of Stacking Ensemble Learning for Classification of COVID-19 using Image Dataset CT Scan and Lung X-Ray. In: 2020 3rd International Conference on Information and Communications Technology (ICOIACT), 2020. 148\u201352. doi: 10.1109\/icoiact50329.2020.9332112","DOI":"10.1109\/ICOIACT50329.2020.9332112"},{"issue":"07","key":"pone.0341198.ref005","article-title":"An interpretation of stacking and blending approach in machine learning","volume":"8","author":"D Khyani","year":"2021","journal-title":"Int Res J Eng Technol"},{"key":"pone.0341198.ref006","doi-asserted-by":"crossref","first-page":"99129","DOI":"10.1109\/ACCESS.2022.3207287","article-title":"A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects","volume":"10","author":"ID Mienye","year":"2022","journal-title":"IEEE Access"},{"key":"pone.0341198.ref007","doi-asserted-by":"crossref","unstructured":"Graczyk M, Lasota T, Trawi\u0144ski B, Trawi\u0144ski K. Comparison of bagging, boosting and stacking ensembles applied to real estate appraisal. In: Intelligent Information and Database Systems: Second International Conference, ACIIDS, Hue City, Vietnam, March 24-26, 2010 Proceedings, Part II, 2010.","DOI":"10.1007\/978-3-642-12101-2_35"},{"key":"pone.0341198.ref008","doi-asserted-by":"crossref","first-page":"107862","DOI":"10.1016\/j.cmpb.2023.107862","article-title":"A simplified coronary model for diagnosis of ischemia-causing coronary stenosis","volume":"242","author":"Y Feng","year":"2023","journal-title":"Comput Methods Programs Biomed"},{"key":"pone.0341198.ref009","doi-asserted-by":"crossref","first-page":"137125","DOI":"10.1016\/j.chemosphere.2022.137125","article-title":"Predicting cumulative lead (Pb) exposure using the Super Learner algorithm","volume":"311","author":"X Wang","year":"2023","journal-title":"Chemosphere"},{"issue":"3","key":"pone.0341198.ref010","article-title":"Evaluation of machine learning models on protein level inference from prioritized RNA features","volume":"23","author":"W Xu","year":"2022","journal-title":"Brief Bioinform"},{"key":"pone.0341198.ref011","doi-asserted-by":"crossref","first-page":"123213","DOI":"10.1016\/j.saa.2023.123213","article-title":"Optimized variable selection and machine learning models for olive oil quality assessment using portable near infrared spectroscopy","volume":"303","author":"R Reda","year":"2023","journal-title":"Spectrochim Acta A Mol Biomol Spectrosc"},{"issue":"1","key":"pone.0341198.ref012","doi-asserted-by":"crossref","first-page":"15626","DOI":"10.1038\/s41598-021-95128-x","article-title":"A stacking ensemble deep learning approach to cancer type classification based on TCGA data","volume":"11","author":"M Mohammed","year":"2021","journal-title":"Sci Rep"},{"issue":"1","key":"pone.0341198.ref013","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1186\/s13014-025-02695-8","article-title":"A stacking ensemble framework integrating radiomics and deep learning for prognostic prediction in head and neck cancer","volume":"20","author":"B Wang","year":"2025","journal-title":"Radiat Oncol"},{"issue":"4","key":"pone.0341198.ref014","doi-asserted-by":"crossref","first-page":"283","DOI":"10.4258\/hir.2019.25.4.283","article-title":"Stacking Ensemble Technique for Classifying Breast Cancer","volume":"25","author":"H Kwon","year":"2019","journal-title":"Healthc Inform Res"},{"key":"pone.0341198.ref015","doi-asserted-by":"crossref","first-page":"125461","DOI":"10.1016\/j.eswa.2024.125461","article-title":"XAI-RACapsNet: Relevance aware capsule network-based breast cancer detection using mammography images via explainability O-net ROI segmentation","volume":"261","author":"A Alhussen","year":"2025","journal-title":"Expert Systems with Applications"},{"issue":"06","key":"pone.0341198.ref016","doi-asserted-by":"crossref","first-page":"2340102","DOI":"10.1142\/S0218348X23401023","article-title":"DCNNBT: A novel deep convolution neural network-based brain tumor classification model","volume":"31","author":"MA Haq","year":"2023","journal-title":"Fractals"},{"issue":"9","key":"pone.0341198.ref017","doi-asserted-by":"crossref","first-page":"1624","DOI":"10.3390\/diagnostics13091624","article-title":"U-Net-Based Models towards Optimal MR Brain Image Segmentation","volume":"13","author":"R Yousef","year":"2023","journal-title":"Diagnostics (Basel)"},{"issue":"1","key":"pone.0341198.ref018","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1007\/s00701-024-06344-z","article-title":"Machine learning for predicting post-operative outcomes in meningiomas: a systematic review and meta-analysis","volume":"166","author":"SY Abualnaja","year":"2024","journal-title":"Acta Neurochir (Wien)"},{"key":"pone.0341198.ref019","article-title":"Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients With Sepsis: Development and Validation Study Based on a Multicenter Cohort Study","volume":"27","author":"J Lei","year":"2025","journal-title":"J Med Internet Res"},{"issue":"12","key":"pone.0341198.ref020","doi-asserted-by":"crossref","first-page":"1302","DOI":"10.1016\/j.jacc.2025.01.030","article-title":"Ensemble deep learning algorithm for structural heart disease screening using electrocardiographic images: PRESENT SHD","volume":"85","author":"LS Dhingra","year":"2025","journal-title":"Journal of the American College of Cardiology"},{"issue":"1","key":"pone.0341198.ref021","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1186\/s13054-020-03179-9","article-title":"Prediction of the development of acute kidney injury following cardiac surgery by machine learning","volume":"24","author":"PY Tseng","year":"2020","journal-title":"Critical Care"},{"key":"pone.0341198.ref022","doi-asserted-by":"crossref","DOI":"10.2196\/67859","article-title":"Machine Learning and Deep Learning Techniques for Prediction and Diagnosis of Leptospirosis: Systematic Literature Review","volume":"13","author":"S Sawesi","year":"2025","journal-title":"JMIR Med Inform"},{"issue":"6","key":"pone.0341198.ref023","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1111\/ejh.14110","article-title":"Artificial intelligence in the prediction of venous thromboembolism: A systematic review and pooled analysis","volume":"111","author":"T Chiasakul","year":"2023","journal-title":"Eur J Haematol"},{"issue":"15","key":"pone.0341198.ref024","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2113561119","article-title":"Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States","volume":"119","author":"EY Cramer","year":"2022","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"1","key":"pone.0341198.ref025","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1186\/s12879-021-06536-3","article-title":"Mortality-related risk factors of COVID-19: a systematic review and meta-analysis of 42 studies and 423,117 patients","volume":"21","author":"ZG Dessie","year":"2021","journal-title":"BMC Infect Dis"},{"key":"pone.0341198.ref026","doi-asserted-by":"crossref","first-page":"107946","DOI":"10.1016\/j.asoc.2021.107946","article-title":"A two-layer nested heterogeneous ensemble learning predictive method for COVID-19 mortality","volume":"113","author":"S Cui","year":"2021","journal-title":"Appl Soft Comput"},{"issue":"1","key":"pone.0341198.ref027","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-0716-2489-0","article-title":"An ensemble prediction model for COVID-19 mortality risk","volume":"7","author":"J Li","year":"2022","journal-title":"Biol Methods Protoc"},{"issue":"1","key":"pone.0341198.ref028","doi-asserted-by":"crossref","first-page":"9782","DOI":"10.1038\/s41598-024-60637-y","article-title":"A novel hybrid supervised and unsupervised hierarchical ensemble for COVID-19 cases and mortality prediction","volume":"14","author":"V Yakovyna","year":"2024","journal-title":"Sci 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Covid-19","author":"M Sperrin","year":"2020","journal-title":"BMJ"},{"issue":"1","key":"pone.0341198.ref033","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1186\/s13054-021-03749-5","article-title":"Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying","volume":"25","author":"MM Banoei","year":"2021","journal-title":"Crit Care"},{"key":"pone.0341198.ref034","article-title":"A systematic review of the application of graph neural networks to extract candidate genes and biological associations","author":"A Saxena","year":"2025","journal-title":"American Journal of Medical Genetics Part B: Neuropsychiatric Genetics"},{"issue":"5","key":"pone.0341198.ref035","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1109\/TCBB.2024.3373772","article-title":"SGLMDA: A Subgraph Learning-Based Method for miRNA-Disease Association Prediction","volume":"21","author":"C Ji","year":"2024","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"5","key":"pone.0341198.ref036","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab074","article-title":"NMCMDA: neural multicategory MiRNA-disease association prediction","volume":"22","author":"J Wang","year":"2021","journal-title":"Brief Bioinform"},{"key":"pone.0341198.ref037","doi-asserted-by":"crossref","first-page":"1079053","DOI":"10.3389\/fgene.2022.1079053","article-title":"Prediction of potential small molecule-miRNA associations based on heterogeneous network representation learning","volume":"13","author":"J Li","year":"2022","journal-title":"Front Genet"},{"issue":"6","key":"pone.0341198.ref038","doi-asserted-by":"crossref","first-page":"100857","DOI":"10.1016\/j.landig.2025.01.013","article-title":"Importance of sample size on the quality and utility of AI-based prediction models for healthcare","volume":"7","author":"RD Riley","year":"2025","journal-title":"Lancet Digit Health"},{"key":"pone.0341198.ref039","doi-asserted-by":"crossref","first-page":"1430245","DOI":"10.3389\/fdgth.2024.1430245","article-title":"A review on over-sampling techniques in classification of multi-class imbalanced datasets: insights for medical problems","volume":"6","author":"Y Yang","year":"2024","journal-title":"Front Digit Health"},{"issue":"10","key":"pone.0341198.ref040","doi-asserted-by":"crossref","DOI":"10.1007\/s10462-024-10884-2","article-title":"Handling imbalanced medical datasets: review of a decade of research","volume":"57","author":"M Salmi","year":"2024","journal-title":"Artif Intell Rev"},{"key":"pone.0341198.ref041","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.infsof.2018.05.007","article-title":"Particle swarm optimization-based ensemble learning for software change prediction","volume":"102","author":"R Malhotra","year":"2018","journal-title":"Information and Software Technology"},{"issue":"1","key":"pone.0341198.ref042","doi-asserted-by":"crossref","first-page":"9782","DOI":"10.1038\/s41598-024-60637-y","article-title":"A novel hybrid supervised and unsupervised hierarchical ensemble for COVID-19 cases and mortality prediction","volume":"14","author":"V Yakovyna","year":"2024","journal-title":"Sci Rep"},{"issue":"10","key":"pone.0341198.ref043","doi-asserted-by":"crossref","first-page":"1875","DOI":"10.1002\/jmv.26050","article-title":"Predictors of mortality in hospitalized COVID-19 patients: A systematic review and meta-analysis","volume":"92","author":"W Tian","year":"2020","journal-title":"J Med Virol"},{"issue":"1","key":"pone.0341198.ref044","doi-asserted-by":"crossref","first-page":"3463","DOI":"10.1038\/s41598-023-28579-z","article-title":"Potential and limitations of machine meta-learning (ensemble) methods for predicting COVID-19 mortality in a large inhospital Brazilian dataset","volume":"13","author":"BBM de Paiva","year":"2023","journal-title":"Sci Rep"},{"key":"pone.0341198.ref045","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/2350063","article-title":"Epidemiology of COVID-19 in Tehran, Iran: A cohort study of clinical profile, risk factors, and outcomes","volume":"2022","author":"H Hatamabadi","year":"2022","journal-title":"BioMed Research International"},{"key":"pone.0341198.ref046","author":"T Klomp","year":"2022"},{"issue":"1","key":"pone.0341198.ref047","doi-asserted-by":"crossref","first-page":"2399","DOI":"10.1038\/s41598-023-28943-z","article-title":"Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features","volume":"13","author":"SS Barough","year":"2023","journal-title":"Sci Rep"},{"issue":"1","key":"pone.0341198.ref048","doi-asserted-by":"crossref","DOI":"10.55938\/ijgasr.v1i1.4","article-title":"A Study on Data Scaling Methods for Machine Learning","volume":"1","author":"V Sharma","year":"2022","journal-title":"INJGAADMIN, ftjijgasr"},{"key":"pone.0341198.ref049","doi-asserted-by":"crossref","unstructured":"Mishra S, Pradhan RK. Analyzing the impact of feature correlation on classification accuracy of machine learning model. In: 2023.","DOI":"10.1109\/AISC56616.2023.10085293"},{"issue":"1","key":"pone.0341198.ref050","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","article-title":"A survey on feature selection methods","volume":"40","author":"G Chandrashekar","year":"2014","journal-title":"Computers & Electrical Engineering"},{"issue":"3","key":"pone.0341198.ref051","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1002\/wics.84","article-title":"Multicollinearity","volume":"2","author":"A Alin","year":"2010","journal-title":"Wiley Interdiscip Rev Comput Stat"},{"key":"pone.0341198.ref052","article-title":"Multicollinearity and regression analysis","author":"JI Daoud","year":"2017","journal-title":"Journal of Physics: Conference Series"},{"key":"pone.0341198.ref053","doi-asserted-by":"crossref","DOI":"10.1201\/9780429186196","volume-title":"Handbook of parametric and nonparametric statistical procedures","author":"DJ Sheskin","year":"2020"},{"issue":"3","key":"pone.0341198.ref054","doi-asserted-by":"crossref","first-page":"3527","DOI":"10.1007\/s12652-020-02592-w","article-title":"RETRACTED ARTICLE: A novel optimal feature selection technique for medical data classification using ANOVA based whale optimization","volume":"12","author":"U Moorthy","year":"2020","journal-title":"J Ambient Intell Human Comput"},{"key":"pone.0341198.ref055","article-title":"Feature Selection Methods and Algorithms","volume":"55","author":"L Ladha","year":"2023","journal-title":"International Journal on Computer Science and Engineering (IJCSE)"},{"issue":"11","key":"pone.0341198.ref056","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pgen.1004754","article-title":"Regularized machine learning in the genetic prediction of complex traits","volume":"10","author":"S Okser","year":"2014","journal-title":"PLoS Genet"},{"key":"pone.0341198.ref057","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v028.i05","article-title":"Building predictive models in R using the caret package","volume":"28","author":"M Kuhn","year":"2008","journal-title":"Journal of Statistical Software"},{"key":"pone.0341198.ref058","volume-title":"Variable selection using the caret package","author":"M Kuhn","year":"2012"},{"key":"pone.0341198.ref059","doi-asserted-by":"crossref","unstructured":"Berrar D. Cross-validation. 2019. 542\u20135.","DOI":"10.1016\/B978-0-12-809633-8.20349-X"},{"issue":"9","key":"pone.0341198.ref060","doi-asserted-by":"crossref","first-page":"893","DOI":"10.3390\/jpm11090893","article-title":"COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal","volume":"11","author":"F Bottino","year":"2021","journal-title":"J Pers Med"},{"key":"pone.0341198.ref061","volume-title":"Machine learning mastery with R: Get started, build accurate models and work through projects step-by-step","author":"J Brownlee","year":"2016"},{"key":"pone.0341198.ref062","volume-title":"Hands-On Ensemble Learning with R: A Beginner\u2019s Guide to Combining the Power of Machine Learning Algorithms Using Ensemble Techniques","author":"PN Tattar","year":"2018"},{"key":"pone.0341198.ref063","doi-asserted-by":"crossref","DOI":"10.1002\/9781118914564","volume-title":"Combining pattern classifiers: methods and algorithms","author":"LI Kuncheva","year":"2014"},{"key":"pone.0341198.ref064","doi-asserted-by":"crossref","first-page":"106517","DOI":"10.1016\/j.knosys.2020.106517","article-title":"A hierarchical fusion framework to integrate homogeneous and heterogeneous classifiers for medical decision-making","volume":"212","author":"L Wang","year":"2021","journal-title":"Knowledge-Based Systems"},{"issue":"3","key":"pone.0341198.ref065","doi-asserted-by":"crossref","first-page":"279","DOI":"10.4300\/JGME-D-12-00156.1","article-title":"Using Effect Size-or Why the P Value Is Not Enough","volume":"4","author":"GM Sullivan","year":"2012","journal-title":"J Grad Med Educ"},{"issue":"1","key":"pone.0341198.ref066","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1037\/a0024338","article-title":"Effect size estimates: current use, calculations, and interpretation","volume":"141","author":"CO Fritz","year":"2012","journal-title":"J Exp Psychol Gen"},{"issue":"1","key":"pone.0341198.ref067","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1080\/19466315.2019.1654912","article-title":"Family-Wise Error Rate Controlling Procedures for Discrete Data","volume":"12","author":"Y Zhu","year":"2019","journal-title":"Statistics in Biopharmaceutical Research"},{"key":"pone.0341198.ref068","volume-title":"Package \u2018pROC\u2019","author":"X Robin","year":"2021"},{"key":"pone.0341198.ref069","volume-title":"Comparing groups: Numerical variables","author":"A Kassambara","year":"2019"},{"key":"pone.0341198.ref070","volume-title":"Package \u2018iml\u2019","author":"C Molnar","year":"2020"},{"key":"pone.0341198.ref071","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511528446","volume-title":"The Shapley value: essays in honor of Lloyd S. Shapley","author":"AE Roth","year":"1988"},{"issue":"1","key":"pone.0341198.ref072","article-title":"ROSE: a package for binary imbalanced learning","volume":"6","author":"N Lunardon","year":"2014","journal-title":"R journal"},{"issue":"1","key":"pone.0341198.ref073","article-title":"Baseline characteristics and associated factors of mortality in COVID-19 patients; an analysis of 16000 cases in Tehran, Iran","volume":"8","author":"A Zali","year":"2020","journal-title":"Arch Acad Emerg Med"},{"key":"pone.0341198.ref074","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.jclinepi.2016.02.031","article-title":"Adequate sample size for developing prediction models is not simply related to events per variable","volume":"76","author":"EO Ogundimu","year":"2016","journal-title":"J Clin Epidemiol"},{"key":"pone.0341198.ref075","doi-asserted-by":"crossref","first-page":"109588","DOI":"10.1016\/j.asoc.2022.109588","article-title":"Handling class imbalance in COVID-19 chest X-ray images classification: Using SMOTE and weighted loss","volume":"129","author":"E Chamseddine","year":"2022","journal-title":"Appl Soft Comput"},{"issue":"1","key":"pone.0341198.ref076","doi-asserted-by":"crossref","first-page":"18478","DOI":"10.1038\/s41598-021-97901-4","article-title":"COVID-19 early detection for imbalanced or low number of data using a regularized cost-sensitive CapsNet","volume":"11","author":"M Javidi","year":"2021","journal-title":"Sci Rep"},{"key":"pone.0341198.ref077","article-title":"Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis","volume":"378","author":"VMT de Jong","year":"2022","journal-title":"BMJ"},{"issue":"1","key":"pone.0341198.ref078","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1186\/s12916-023-02779-w","article-title":"There is no such thing as a validated prediction model","volume":"21","author":"B Van Calster","year":"2023","journal-title":"BMC Med"},{"key":"pone.0341198.ref079","doi-asserted-by":"crossref","first-page":"109853","DOI":"10.1016\/j.chaos.2020.109853","article-title":"Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil","volume":"135","author":"MHDM Ribeiro","year":"2020","journal-title":"Chaos Solitons Fractals"},{"issue":"1","key":"pone.0341198.ref080","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1177\/10815589231201103","article-title":"Clinical predictions of COVID-19 patients using deep stacking neural networks","volume":"72","author":"S Hussain","year":"2024","journal-title":"J Investig Med"},{"issue":"3","key":"pone.0341198.ref081","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1111\/acem.12876","article-title":"Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach","volume":"23","author":"RA Taylor","year":"2016","journal-title":"Acad Emerg Med"},{"issue":"1","key":"pone.0341198.ref082","doi-asserted-by":"crossref","first-page":"3463","DOI":"10.1038\/s41598-023-28579-z","article-title":"Potential and limitations of machine meta-learning (ensemble) methods for predicting COVID-19 mortality in a large inhospital Brazilian dataset","volume":"13","author":"BBM de Paiva","year":"2023","journal-title":"Sci Rep"},{"key":"pone.0341198.ref083","doi-asserted-by":"crossref","first-page":"103411","DOI":"10.1016\/j.jbi.2020.103411","article-title":"Deep ensemble learning for Alzheimer\u2019s disease classification","volume":"105","author":"N An","year":"2020","journal-title":"J Biomed Inform"},{"issue":"4","key":"pone.0341198.ref084","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1007\/s00354-021-00144-0","article-title":"Stacking Ensemble-Based Intelligent Machine Learning Model for Predicting Post-COVID-19 Complications","volume":"40","author":"A Gupta","year":"2022","journal-title":"New Gener Comput"},{"key":"pone.0341198.ref085","doi-asserted-by":"crossref","first-page":"105090","DOI":"10.1016\/j.ijmedinf.2023.105090","article-title":"Evaluation of stacked ensemble model performance to predict clinical outcomes: A COVID-19 study","volume":"175","author":"R Kablan","year":"2023","journal-title":"Int J Med Inform"},{"issue":"1","key":"pone.0341198.ref086","article-title":"Neutrophil-to-lymphocyte ratio as an independent risk factor for mortality in hospitalized patients with COVID-19","volume":"81","author":"Y Liu","year":"2020","journal-title":"J Infect"},{"issue":"7","key":"pone.0341198.ref087","doi-asserted-by":"crossref","first-page":"934","DOI":"10.1001\/jamainternmed.2020.0994","article-title":"Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients With Coronavirus Disease 2019 Pneumonia in Wuhan, China","volume":"180","author":"C Wu","year":"2020","journal-title":"JAMA Intern Med"},{"issue":"1","key":"pone.0341198.ref088","doi-asserted-by":"crossref","first-page":"2933","DOI":"10.1038\/s41598-021-82492-x","article-title":"Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning","volume":"11","author":"W Xu","year":"2021","journal-title":"Sci Rep"},{"key":"pone.0341198.ref089","article-title":"Retrospective cohort studies: advantages and disadvantages","volume":"348","author":"P Sedgwick","year":"2014","journal-title":"BMJ"},{"key":"pone.0341198.ref090","article-title":"Biases in electronic health record data due to processes within the healthcare system: retrospective observational study","volume":"361","author":"D Agniel","year":"2018","journal-title":"BMJ"},{"key":"pone.0341198.ref091","article-title":"Inherent Bias in Electronic Health Records: A Scoping Review of Sources of Bias","author":"O Perets","year":"2024","journal-title":"medRxiv"},{"issue":"1","key":"pone.0341198.ref092","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1146\/annurev-statistics-042522-103837","article-title":"A Review of Generalizability and Transportability","volume":"10","author":"I Degtiar","year":"2023","journal-title":"Annu Rev Stat Appl"},{"key":"pone.0341198.ref093","doi-asserted-by":"crossref","DOI":"10.1136\/bmj-2023-074819","article-title":"Evaluation of clinical prediction models (part 1): from development to external validation","volume":"384","author":"GS Collins","year":"2024","journal-title":"BMJ"},{"issue":"2","key":"pone.0341198.ref094","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1161\/CIRCULATIONAHA.114.014508","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. 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