Severity and Outcome Detection for the Cancer Patients with COVID-19 Using Machine Learning Models (preprint)

Background: The COVID-19 pandemic challenges the healthcare system to provide enough resources to battle the pandemic without jeopardizing routine treatments. As a result, this is important that we can predict the outcomes of patients at the time of admission. This study aims to apply different machine learning (ML) models for predicting Intensive Care Unit (ICU) admission and mortality of Cancer Patients infected with COVID-19. Methods: This study's data were collected from a referral cancer center in Iran. The study included all patients with cancer and a confirmed diagnosis of COVID-19.Different ML prediction algorithms like Logistic Regression (LR), Naive Bayes (NB), k-Nearest Neighbours (kNN), Random Forest (RF), and Support Vector Machine (SVM) were used. Also, we applied the SelectKBest method to find the most important features for predicting ICU admission and mortality. Results: Three hundred thirty-nine patients enrolled in the study. One hundred fifteen were admitted to the Intensive Care Unit (ICU), and 118 patients died during the hospital admission. The Area Under Curve (AUC) for predicting mortality is 0.61 for LR, 0.74 for NB, 0.61 for kNN, 0.6 for SVM, and 0.79 for RF. The AUC for predicting ICU admission is 0.61 for LR, 0.74 for NB, 0.56 for kNN, 0.55 for SVM, and 0.7 for RF.C-reactive protein (CRP), Aspartate transaminase (AST), and Neutrophil-Lymphocyte Ratio (NLR) also are the most common features in predicting ICU admission and mortality. Conclusion: Our findings show the promise of different AI methods for predicting the risk of death or ICU in cancer patients infected with COVID-19, highlighting the importance of first laboratory results and patients' symptoms.