Machine learning approaches for screening the risk of obstructive sleep apnea in the Taiwan population based on body profile

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.

KEYWORDS:

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

Funding

This study was funded by the Ministry of Science and Technology, Taiwan (MOST 108-2634-F-038-003) . The funder had no role in the study design, data collection and analysis, or writing of the manuscript.

Acknowledgments

We would like to thank all participants for their contribution to this research.

Availability of Data and Materials

All the data of this study were derived from the Sleep Center of Taipei Medical University Shuang Ho Hospital (New Taipei City, Taiwan) between March 2015 and Jan 2021. Since there was personal information within the dataset, the dataset was not available in the supplement file. Please get in touch with the corresponding author to require the dataset or relevant documents if needed.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethics approval and consent to participate

Existing records were used to conduct this retrospective study. All necessary approvals and a waiver of informed consent were obtained from the Ethics Committee of the Taipei Medical University–Joint Institutional Review Board (SHH: N201911007).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.