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In recent years, mortality prediction models have shown a potential in alleviating this issue; however, these models are susceptible to biases in specific subpopulations with different risks of mortality, such as patients with prior history of smoking. The current study aims to develop a machine learning-based mortality prediction model for COVID-19 patients that have a history of smoking in the Iranian population.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>A retrospective study was conducted across six medical centers between 18 and 2020 and 15 March 2022, comprised of 678 CT scans and laboratory-confirmed COVID-19 patients that had a history of smoking. Multiple machine learning models were developed using 10-fold cross-validation. The target variable was in-hospital mortality and input features included patient demographics, levels of care, vital signs, medications, and comorbidities. Two sets of models were developed for at-admission and post-admission predictions. Subsequently, the top five prediction models were selected from at-admission models and post-admission models and their probabilities were calibrated.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The in-hospital mortality rate for smoker COVID-19 patients was 20.1%. For \u201cat admission\u201d models, the best-calibrated model was XGBoost which yielded an accuracy of 87.5% and F<jats:sub>1<\/jats:sub> score of 86.2%. For the \u201cpost-admission\u201d models, XGBoost also outperformed the rest with an accuracy of 90.5% and F<jats:sub>1<\/jats:sub> score of 89.9%. Active smoking was among the most important features in patients\u2019 mortality prediction.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Our machine learning-based mortality prediction models have the potential to be adapted for improving the management of smoker COVID-19 patients and predicting patients\u2019 chance of survival.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-023-02237-w","type":"journal-article","created":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T11:02:06Z","timestamp":1689937326000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Machine learning-based mortality prediction models for smoker COVID-19 patients"],"prefix":"10.1186","volume":"23","author":[{"given":"Ali","family":"Sharifi-Kia","sequence":"first","affiliation":[]},{"given":"Azin","family":"Nahvijou","sequence":"additional","affiliation":[]},{"given":"Abbas","family":"Sheikhtaheri","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,21]]},"reference":[{"issue":"5","key":"2237_CR1","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1038\/s42256-020-0180-7","volume":"2","author":"L Yan","year":"2020","unstructured":"Yan L, Zhang HT, Goncalves J, Xiao Y, Wang M, Guo Y, Sun C, Tang X, Jing L, Zhang M, et al. 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