Comparing of Machine Learning Algorithms for Predicting ICU admission in COVID-۱۹ hospitalized patients

Aims: The world hospital systems are presently facing many unprecedented challenges from COVID‐۱۹ disease. Prediction the deteriorating or critical cases can help triage patients and assist in effective medical resource allocation. This study aimed to develop and validate a prediction model based on Machine Learning algorithms to predict hospitalized COVID-۱۹ patients for transfer to ICU based on clinical parameters. Materials & Methods: This retrospective, single-center study was conducted based on cumulative data of COVID-۱۹ patients (N=۱۲۲۵) who were admitted from March ۹, ۲۰۲۰, to December ۲۰, ۲۰۲۰, to Mostafa Khomeini Hospital, affiliated to Ilam University of Medical Sciences (ILUMS), focal point center for COVID-۱۹ care and treatment in Ilam, West of Iran. ۱۳ ML techniques from six different groups applied to predict ICU admission. To evaluate the performances of models, the metrics derived from the confusion matrix were calculated. The algorithms were implemented using WEKA ۳.۸ software. Findings: This retrospective studychr('۳۹')s median age was ۵۰.۹ years, and ۶۶۴ (۵۴.۲%) were male. The experimental results indicate that Meta algorithms have the best performance in ICU admission risk prediction with an accuracy of ۹۰.۳۷%, a sensitivity of ۹۰.۳۵%, precision of ۸۸.۲۵%, F-measure of ۸۸.۳۵%, and ROC of ۹۱%. Conclusion: Machine Learning algorithms are helpful predictive tools for real-time and accurate ICU risk prediction in patients with COVID-۱۹ at hospital admission. This model enables and potentially facilitates more responsive health systems that are beneficial to high-risk COVID-۱۹ patients.

کلیدواژه ها:

COVID‐۱۹ ، Coronavirus ، Machine Learning ، Artificial Intelligence ، Forecasting ، Intensive Care Unit