Towards Effective and Reliable Data-driven Prognostication: An Application to COVID-19

Abstract

This study evaluates machine learning methods to predict the prognosis of patients in COVID-19 context. In addition, considering the best-performing machine learning algorithm, we applied the LIME explanation technique for machine learning models to verify how the features correlate with each decision made, in order to assist an expert regarding the groundings of the decision made by the model. The results reveal that the model developed was able to predict the patient’s prognosis with an ROC-AUC = 0.8524. The prediction explanations allowed us to understand how each feature contributes to the decision made by the model, thus bringing transparency to the developed model.

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