{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T18:33:26Z","timestamp":1789670006008,"version":"4.0.1"},"reference-count":67,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:00:00Z","timestamp":1739145600000},"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:p>The healthcare industry is generating a massive volume of data, promising a potential goldmine of information that can be extracted through machine learning (ML) techniques. The Intensive Care Unit (ICU) stands out as a focal point within hospitals and provides a rich source of data for informative analyses. This study examines the cardiac surgery ICU, where the vital topic of patient ventilation takes center stage. In other words, ventilator-supported breathing is a fundamental need within the ICU, and the limited availability of ventilators in hospitals has become a significant issue. A crucial consideration for healthcare professionals in the ICU is prioritizing patients who require ventilators immediately. To address this issue, we developed a prediction model using four ML and deep learning (DL) models\u2014LDA, CatBoost, Artificial Neural Networks (ANN), and XGBoost\u2014that are combined in an ensemble model. We utilized Simulated Annealing (SA) and Genetic Algorithm (GA) to tune the hyperparameters of the ML models constructing the ensemble. The results showed that our approach enhanced the sensitivity of the tuned ensemble model to 85.84%, which are better than the results of the ensemble model without hyperparameter tuning and those achieved using AutoML model. This significant improvement in model performance underscores the effectiveness of our hybrid approach in prioritizing the need for ventilators among ICU patients.<\/jats:p>","DOI":"10.1371\/journal.pone.0311250","type":"journal-article","created":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T13:30:06Z","timestamp":1739194206000},"page":"e0311250","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":14,"title":["Enhancing machine learning performance in cardiac surgery ICU: Hyperparameter optimization with metaheuristic algorithm"],"prefix":"10.1371","volume":"20","author":[{"given":"Ali","family":"Bahrami","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5250-9165","authenticated-orcid":true,"given":"Morteza","family":"Rakhshaninejad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5839-5792","authenticated-orcid":true,"given":"Rouzbeh","family":"Ghousi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alireza","family":"Atashi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,2,10]]},"reference":[{"key":"pone.0311250.ref001","doi-asserted-by":"crossref","first-page":"141066","DOI":"10.1109\/ACCESS.2020.3013320","article-title":"A New Hybrid Predictive Model to Predict the Early Mortality Risk in Intensive Care Units on a Highly Imbalanced Dataset","volume":"8","author":"R Ghorbani","year":"2020","journal-title":"IEEE Access"},{"key":"pone.0311250.ref002","first-page":"1","article-title":"An ensemble data mining approach to discover medical patterns and provide a system to predict the mortality in the ICU of cardiac surgery based on stacking machine learning method","author":"A Ghavidel","year":"2022","journal-title":"Comput Methods Biomech Biomed Eng Imaging Vis"},{"key":"pone.0311250.ref003","doi-asserted-by":"crossref","first-page":"215","DOI":"10.5603\/AIT.a2016.0036","article-title":"APACHE II score predicts mortality in patients requiring prolonged ventilation in a weaning center","volume":"48","author":"A Rojek-Jarmu\u0142a","year":"2016","journal-title":"Anaesthesiol Intensive Ther"},{"key":"pone.0311250.ref004","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1177\/0310057X0903700515","article-title":"The performance of customised APACHE II and SAPS II in predicting mortality of mixed critically ill patients in a Thai medical intensive care unit","volume":"37","author":"B Khwannimit","year":"2009","journal-title":"Anaesth Intensive Care"},{"key":"pone.0311250.ref005","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.1007\/s00134-012-2578-0","article-title":"Comparison between SAPS II and SAPS 3 in predicting hospital mortality in a cohort of 103 Italian ICUs. 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