Machine Learning Models to Discriminate COVID-19 Severity with Biomarkers Available in Brazilian Public Health

Despite advances in vaccination and treatment, the emergence of Long COVID cases has highlighted the continued public health concern posed by the disease. Studies on the early prediction of COVID-19 severity and the identification of associated biomarkers are decisive for preventing Long COVID. The objective is to utilise laboratory test data from patients diagnosed with COVID-19 and apply machine learning techniques to predict disease severity and identify associated biomarkers. From a university hospital in southern Brazil, we processed biochemical and haematological data from patients with COVID-19 (non-severe = non-ICU admission; severe = ICU admission). The data were used to train 15 machine learning algorithms to predict patient prognosis. The Light Gradient Boosting Machine (LGBM) model demonstrated the most effective performance in predicting the prognosis of patients with COVID-19, with accuracy, sensitivity, specificity, and precision values between 80 and 88%. Biomarkers associated with disease severity included Platelets, Creatinine, Erythrocytes, C-reactive protein, Lymphocytes, Albumin, Glucose, Urea, and Sodium. The results of this study demonstrate that machine learning, particularly LGBM, is an effective method for predicting the severity of COVID-19. Identifying specific biomarkers associated with disease severity is crucial for the early intervention and prevention of Long COVID, thereby improving clinical outcomes and patient management. LGBM maintained its performance across different age groups.