(a) Objective: Obstructive sleep apnea syndrome (OSAS) is typically diagnosed through polysomnography (PSG). However, PSG incurs high medical costs. This study developed new models for screening the risk of moderate-to-severe OSAS (apnea-hypopnea index, AHI ≥15) and severe OSAS (AHI ≥30) in various age groups and sexes by using anthropometric features in the Taiwan population. (b) Participants: Data were derived from 10,391 northern Taiwan patients who underwent PSG. (c) Methods: Patients’ characteristics–namely age, sex, body mass index (BMI), neck circumference, and waist circumference–was obtained. To develop an age- and sex-independent model, various approaches–namely logistic regression, k-nearest neighbor, naive Bayes, random forest (RF), and support vector machine–were trained for four groups based on sex and age (men or women; aged <50 or ≥50 years). Dataset was separated independently (training:70%; validation: 10%; testing: 20%) and Cross-validated grid search was applied for model optimization. Models demonstrating the highest overall accuracy in validation outcomes for the four groups were used to predict the testing dataset. (d) Results: The RF models showed the highest overall accuracy. BMI was the most influential parameter in both types of OSAS severity screening models. (e) Conclusion: The established models can be applied to screen OSAS risk in the Taiwan population and those with similar craniofacial features.

Original languageEnglish
JournalInformatics for Health and Social Care
Publication statusAccepted/In press - 2021


  • anthropometric features
  • apnea-hypopnea index
  • Obstructive sleep apnea syndrome
  • polysomnography
  • random forest model
  • sleep disorder indexes

ASJC Scopus subject areas

  • Health Informatics
  • Nursing (miscellaneous)
  • Health Information Management


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