EXPLAINABLE ARTIFICIAL INTELLIGENCE APPLIED TO PREDICTING HEALTH OUTCOMES
Techniques based on Artificial Intelligence show promise in decision support in the health area, in which several studies show computational efficiency in processing large amounts of data and its effectiveness in contributing to decisions based on the information obtained. However, the lack of understanding of the models' predictive mechanisms has led to the search for new techniques based on Explainable Artificial Intelligence that allow elucidating questions about how predictions are made.In this context, this work sought to develop a method based on Explainable Artificial Intelligence demonstrating the relationship between input data and the predictive response. In the preliminary results, two methods of explainability were considered (Local Interpretable Model-agnostic Explanation and Shapley Additive Explanations) in a breast tumor classification model, and it was concluded that the model's explainability indicated relevant characteristics in the classification of the type of tumor.