Título: Explainable Artificial Intelligence in the Estimation of Sleep Apnea Severity Using Heart Rate Variability
Autores: Gabriel Branco Vitorino, Rafael Rodrigues dos Santos, Prof. Dr. Luiz Eduardo V. Silva, Alan Luiz Eckeli, Rubens Fazan Junior & Renato Tinós
Resumo: An Explainable Artificial Intelligence algorithm is used to generate explanations of the decisions of Machine Learning models trained to classify the severity of Obstructive Sleep Apnea based on Heart Rate Variability. First, a Random Forest classifier is used for Obstructive Sleep Apnea severity prediction and then the Local Rule-based Explanations method is applied to provide decisions interpretability. The method employs Genetic Algorithms to generate artificial examples, which are then used to train decision trees that approximate the local behavior of the black-box model. This approach enables the extraction of human-readable rules that explain individual predictions, offering valuable insights into the decision-making process of black-box models such as Random Forests. By identifying the most influential features and decision rules, our approach aims to aid medical professionals in understanding how Heart Rate Variability correlates with Obstructive Sleep Apnea severity and enhances trust in Machine Learning-driven diagnostic tools.
Palavras-chave: Explainable Artificial Intelligence; Genetic Algorithms; Heart Rate Variability; Obstructive Sleep Apnea.
Páginas: 8
Código DOI: 10.21528/CBIC2025-1167061
Artigo em PDF: CBIC_2025_paper1167061.pdf
Arquivo BibTeX:
CBIC_2025_1167061.bib