1. Machine learning approaches for screening the risk of obstructive sleep apnea in the Taiwan population based on body profile.
- Author
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Tsai CY, Liu WT, Lin YT, Lin SY, Houghton R, Hsu WH, Wu D, Lee HC, Wu CJ, Li LYJ, Hsu SM, Lo CC, Lo K, Chen YR, Lin FC, and Majumdar A
- Subjects
- Male, Humans, Female, Taiwan epidemiology, Bayes Theorem, Polysomnography, Machine Learning, Sleep Apnea, Obstructive diagnosis, Sleep Apnea, Obstructive epidemiology
- Abstract
(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.
- Published
- 2022
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