Using Artificial Intelligence-based models to predict the risk of Mucormycosis among COVID-19 Survivors: An Experience from India

Introduction: India reported a severe public health challenge not only due to the COVID-19 outbreak but also the increasing number of associated mucormycosis cases since 2021. This study aimed at developing artificial intelligence-based models to predict the risk of mucormycosis among the patients at the time of discharge from the hospital. Methods: The dataset included 1229 COVID-19 positive patients, and additional 214 inpatients, COVID-19 positive as well as infected with mucormycosis. We used logistic regression, decision tree, and random forest, and the extreme gradient boosting algorithm. All our models were evaluated with 5-fold validation to derive a reliable estimate of the model error. Results: The logistic regression, XGBoost, and random forest performed equally well with AUROC 95.0, 94.0, and 94.0 respectively. This study also determined the top five variables namely obesity, anosmia, de novo diabetes, myalgia, and nasal discharge, which showed a positive impact on the risk of mucormycosis. Conclusion: The developed model has the potential to predict the patients at high risk and thus, consequently initiating preventive care or aiding in early detection of mucormycosis infection. Thus, this study holds potential for early treatment and better management of patients suffering from COVID-19 associated mucormycosis.