Comparing of Machine Learning Algorithms for Predicting ICU admission in COVID-19 hospitalized patients

Aims: The world hospital systems are presently facing many unprecedented challenges from COVID‐19 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 COVID19 patients for transfer to ICU based on clinical parameters. Materials Methods: This retrospective, single center study was conducted based on cumulative data of COVID19 patients (N=1225) who were admitted from March 9, 2020, to December 20, 2020, to Mostafa Khomeini Hospital, affiliated to Ilam University of Medical Sciences (ILUMS), focal point center for COVID19 care and treatment in Ilam, West of Iran. 13 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 3.8 software. Findings: This retrospective study median age was 50.9 years, and 664 (54.2%) were male. The experimental results indicate that Meta algorithms have the best performance in ICU admission risk prediction with an accuracy of 90.37%, a sensitivity of 90.35%, precision of 88.25%, Fmeasure of 88.35%, and ROC of 91%. Conclusion: Machine Learning algorithms are helpful predictive tools for realtime and accurate ICU risk prediction in patients with COVID19 at hospital admission. This model enables and potentially facilitates more responsive health systems that are beneficial to high risk COVID19 patients.