An Intelligent System for Prediction of Severity of SARS-Cov-2 Infection and Progression to Critical Illness: Using Machine Learning Models

Abstract Introduction: The rapid worldwide outbreak of coronavirus disease 2019 (COVID-19) has posed serious and extraordinary challenges to healthcare industries in predicting disease behavior, and outcomes. Aim: This study aimed to develop a Clinical Decision Support System (CDSS) for predicting the severity of SARS-CoV-2 infection and progression to critical illness in a patient with COVID-19 using several machine learning algorithms. Material and Methods: Using a two-center registry, the data of 2482 COVID-19 patients from February 9, 2020, to December 20, 2020, were reviewed. The Relief Feature Selection (RFS) algorithm was used for optimizing the input variables. Then, selected variables feed into ML models including XGBoost, HistGradient Boosting (HGB), Random Forest (RF), and Naïve Bayesian (NB) to construct prediction models. Afterwards, the performance of each combination was compared using some evaluation metrics. Eventually using the best ML model performance, a Clinical Decision Support System (CDSS) was implemented with C# programming language.Results: of the 63 included variables, 15 features were identified as the most important predictors. The experimental results indicated that the HGB classifier with an average classification accuracy of 94.2%, mean specificity of 92.4%, mean sensitivity of 91%, mean F-score of 87.2 %, and finally mean AUC of 87.3 % was selected as the most appropriate machine learning model for predicting the Severity of SARS-CoV-2.Conclusion: The results of this study showed that the hybrid ML algorithms and in particular the RFS-HGB (by optimizing input variables and customizing the structure of the algorithms (can help the frontline clinicians to predict the severity of COVID-19 progression.