Use of Genetic Algorithms to Estimate the Interaction and Importance of Variables in the Classification of Severity of Sleep Apnea

• Grant number: 24/10564-3
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: October 01, 2024
• End date: September 30, 2025
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Renato Tinós
• Grantee: Sabrina Sousa Carvalho
• Host Institution: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil

Abstract

Obstructive Sleep Apnea is one of the most common sleep disorders, consisting of the cessation or reduction of airflow during sleep. The most common and accurate test to diagnose sleep disorders is polysomnography which, despite its effectiveness, is a complex and relatively expensive test. Recently, the use of Heart Rate Variability and Machine Learning (ML) to classify the severity of Sleep Apnea has been investigated. In this project, we will apply the Genetic Algorithm with Linkage Learning (GAwLL) to select features in the problem of sleep apnea severity classification. GAwLL discovers a graph of interactions between variables as a side effect of the optimization performed by a simple Genetic Algorithm. Discovering which features are important and how they interact in an AM model for decision making is relevant for the understanding, by specialists in the field of Medicine, of how Heart Rate Variability is related to Sleep Apnea.