Exploring the complexity of obstructive sleep apnea: findings from machine learning on diagnosis and predictive capacity of individual factors.

Journal: Sleep & breathing = Schlaf & Atmung
Published Date: Dec 5, 2024

Abstract

PURPOSE: Obstructive sleep apnoea (OSA) is a prevalent sleep disorder characterized by pharyngeal airway collapse during sleep, leading to intermittent hypoxia, intrathoracic pressure swings, and sleep fragmentation. OSA is associated with various comorbidities and risk factors, contributing to its substantial economic and social burden. Machine learning (ML) techniques offer promise in predicting OSA severity and understanding its complex pathogenesis. This study aims to compare the accuracy of different ML techniques in predicting OSA severity and identify key associated factors contributing to OSA.

Authors

  • Simone Russo
    Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, Italian Workers' Compensation Authority (INAIL), Via Fontana Candida 1, Monte Porzio Catone, 00078, Rome, Italy. si.russo@inail.it.
  • Agnese Martini
    Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, Italian Workers' Compensation Authority (INAIL), Via Fontana Candida 1, Monte Porzio Catone, 00078, Rome, Italy.
  • Valeria Luzzi
    Department of Oral and Maxillofacial Sciences, UOC Paediatric Dentistry, Sapienza University of Rome, Rome, Italy.
  • Sergio Garbarino
    Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal, Child Sciences (DINOGMI), University of Genoa, Genoa, Italy.
  • Emma Pietrafesa
    Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, Italian Workers' Compensation Authority (INAIL), Via Fontana Candida 1, Monte Porzio Catone, 00078, Rome, Italy.
  • Antonella Polimeni
    Department of Oral and Maxillo Facial Sciences, Policlinico Umberto I, "Sapienza" University of Rome, Rome, Italy.

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