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, cilt.56, sa.4, ss.308-318, 2017 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 56 Sayı: 4
  • Basım Tarihi: 2017
  • Doi Numarası: 10.3414/me16-01-0084
  • Dergi Adı: METHODS OF INFORMATION IN MEDICINE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.308-318
  • Anahtar Kelimeler: Obstructive sleep apnea, machine learning, diagnostic accuracy, Bayesian networks, PHYSICAL-EXAMINATION, PREDICTION MODEL, EPIDEMIOLOGY
  • Akdeniz Üniversitesi Adresli: Evet

Özet

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.