Can Statistical Machine Learning Algorithms Help for Classification of Obstructive Sleep Apnea Severity to Optimal Utilization of Polysomnography Resources?


Bozkurt S., Bostanci A., TURHAN M.

METHODS OF INFORMATION IN MEDICINE, vol.56, no.4, pp.308-318, 2017 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 56 Issue: 4
  • Publication Date: 2017
  • Doi Number: 10.3414/me16-01-0084
  • Journal Name: METHODS OF INFORMATION IN MEDICINE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.308-318
  • Keywords: Obstructive sleep apnea, machine learning, diagnostic accuracy, Bayesian networks, PHYSICAL-EXAMINATION, PREDICTION MODEL, EPIDEMIOLOGY
  • Akdeniz University Affiliated: Yes

Abstract

Objectives: The goal of this study is to evaluate the results of machine learning methods for the classification of OSA severity of patients with suspected sleep disorder breathing as normal, mild, moderate and severe based on non-polysomnographic variables: 1) clinical data, 2) symptoms and 3) physical examination.