Abstract
INTRODUCTION: Previous studies applying machine learning to predict severe respiratory failure in COVID-19 patients have shown inconsistent results due to variations in study populations and predictor variables. This study aimed to predict intensive care unit admission and identify key predictive factors. METHODS: This retrospective cohort study included patients with COVID-19 who presented with mild respiratory failure, most of whom received oxygen via a mask or nasal cannula. Eight machine learning algorithms-XGBoost, support vector machines, neural networks, k-nearest neighbors, random forest, decision trees, logistic regression, and naïve Bayes-were applied to predict intensive care unit admission. RESULTS: A total of 392 patients (63.5% male, mean age, 55.0 ± 15.3 years) were included in the study. During follow-up, 80 patients (20.4%) required intensive care unit admission. Among them, 320 (81.6%) received steroid therapy, 301 (76.8%) underwent pulse steroid therapy, and 76 (19%) had been vaccinated. The multilayer perceptron, XGBoost, and radial basis function support vector machine models achieved the best overall performance based on ROC-AUC and accuracy values (ROC-AUC: 0.75, 0.70, and 0.71; accuracy: 0.79, 0.79, and 0.79, respectively). The strongest predictors of intensive care unit admission were low lymphocyte count on the first day, as well as high age, ferritin, body mass index, Charlson Comorbidity Index, and computed tomography score. CONCLUSION: Machine learning algorithms can reliably predict intensive care unit admission in COVID-19 patients with mild respiratory failure. These models identified key clinical and laboratory factors that may facilitate early risk stratification and guide treatment planning.