Machine learning-based mortality prediction models for smoker COVID-19 patients

BACKGROUND: The large number of SARS-Cov-2 cases during the COVID-19 global pandemic has burdened healthcare systems and created a shortage of resources and services. 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. METHODS: 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. RESULTS: The in-hospital mortality rate for smoker COVID-19 patients was 20.1%. For “at admission” models, the best-calibrated model was XGBoost which yielded an accuracy of 87.5% and F(1) score of 86.2%. For the “post-admission” models, XGBoost also outperformed the rest with an accuracy of 90.5% and F(1) score of 89.9%. Active smoking was among the most important features in patients’ mortality prediction. CONCLUSION: 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’ chance of survival. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-023-02237-w.