Enhancing COVID-19 Prognosis Prediction with Machine Learning and LIME Explanation - Solenir Lima Figuerêdo, José; Freitas Araújo-Calumby, Renata; Tripodi Calumby, Rodrigo (Journal of Information and Data Management, 2025)
This study evaluates machine learning methods to predict the prognosis of patients in COVID-19 context. This study evaluates machine learning methods for predicting patient prognosis in the COVID-19 context. For the best-performing algorithm, we applied LIME to assess feature contributions to each decision, providing insights to assist experts in understanding the rationale behind the model's predictions. The results indicate that the developed model accurately predicted patient prognosis, achieving an ROC-AUC = 0.8524. The results also point out a higher risk of death among patients over 60 years of age, with comorbidities, and symptoms such as dyspnea and Oxygen saturation <95%, confirming results observed in other regions of the world. The results also indicated a higher percentage of deaths among those with little or no education. The prediction explanations allowed us to understand how each feature contributes to the decision made by the model, improving its transparency. For instance, in an illustrative case, LIME demonstrated that invasive ventilatory support and an age of 61 years positively contributed to the prediction of mortality, whereas hospitalization and the patient's race (being white) were not significant predictors for this particular patient.