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

Resumo


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.
Palavras-chave: COVID-19, Machine Learning, Computer aided prognosis, Mortality prediction, Explainable AI

Referências

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Publicado
26/09/2023
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FIGUERÊDO, José Solenir Lima; ARAUJO-CALUMBY, Renata Freitas; CALUMBY, Rodrigo Tripodi. Towards Effective and Reliable Data-driven Prognostication: An Application to COVID-19. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 11. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 81-88. ISSN 2763-8944. DOI: https://doi.org/10.5753/kdmile.2023.232894.