Machine learning approaches for screening the risk of obstructive sleep apnea in the Taiwan population based on body profile.
Inform Health Soc Care
; 47(4): 373-388, 2022 Oct 02.
Article
em En
| MEDLINE
| ID: mdl-34886766
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 (training70%; 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.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Apneia Obstrutiva do Sono
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article