This work proposes an explainable artificial intelligence approach to help diagnose COVID-19 patients based on blood test and pathogen variables. Two glass-box models, logistic regression and explainable boosting machine, and two black-box models, random forest and support vector machine, were used to assess the disease diagnosis. Shapley additive explanations were used to explain predictions for the black-box models, while glass-box models feature importance brought insights into the most relevant features. All global explanations show the eosinophils and leukocytes, white blood cells are among the essential features to help diagnose the COVID-19. Moreover, the best model obtained an AUC of 0.87.

This work proposes an interpretable machine learning approach to diagnosesuspected COVID-19 cases based on clinical variables. Results obtained for the proposed models have F-2 measure superior to 0.80 and accuracy superior to 0.85. Interpretation of the linear model feature importance brought insights about the most relevant features. Shapley Additive Explanations were used in the non-linear models. They were able to show the difference between positive and negative patients as well as offer a global interpretability sense of the models.

Este trabalho propõe uma nova metodologia de cálculo dos limites coletivos dos indicadores de continuidade das distribuidoras de energia elétrica no Brasil. Esta metodologia tem como base a execução de uma clusterização fuzzy otimizada, utilizando o algoritmos Fuzzy C-Means e de Otimização por Enxame de Partículas, com o intuito de flexibilizar o agrupamento de conjuntos de unidades consumidoras e o cálculo de metas para seus indicadores coletivos de continuidade. A clusterização foi realizada por regiões do Brasil e os resultados obtidos são comparáveis à metodologia atual, porém com uma trajetória de redução mais acentuada.