Imperial College London
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ProfessorArnabMajumdar

Faculty of Engineering, Department of Civil and Environmental Engineering

Professor of Transport Risk and Safety
 
 
 
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Contact

 

+44 (0)20 7594 6037a.majumdar

 
 
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Assistant

 

Ms Maya Mistry +44 (0)20 7594 6100

 
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Location

 

604Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Tsai:2022:10.1080/17538157.2021.2007930,
author = {Tsai, C-Y and Liu, W-T and Lin, Y-T and Lin, S-Y and Houghton, R and Hsu, W-H and Wu, D and Lee, H-C and Wu, C-J and Li, LYJ and Hsu, S-M and Lo, C-C and Lo, K and Chen, Y-R and Lin, F-C and Majumdar, A},
doi = {10.1080/17538157.2021.2007930},
journal = {Inform Health Soc Care},
pages = {373--388},
title = {Machine learning approaches for screening the risk of obstructive sleep apnea in the Taiwan population based on body profile.},
url = {http://dx.doi.org/10.1080/17538157.2021.2007930},
volume = {47},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - (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.
AU  - Tsai,C-Y
AU  - Liu,W-T
AU  - Lin,Y-T
AU  - Lin,S-Y
AU  - Houghton,R
AU  - Hsu,W-H
AU  - Wu,D
AU  - Lee,H-C
AU  - Wu,C-J
AU  - Li,LYJ
AU  - Hsu,S-M
AU  - Lo,C-C
AU  - Lo,K
AU  - Chen,Y-R
AU  - Lin,F-C
AU  - Majumdar,A
DO  - 10.1080/17538157.2021.2007930
EP  - 388
PY  - 2022///
SP  - 373
TI  - Machine learning approaches for screening the risk of obstructive sleep apnea in the Taiwan population based on body profile.
T2  - Inform Health Soc Care
UR  - http://dx.doi.org/10.1080/17538157.2021.2007930
UR  - https://www.ncbi.nlm.nih.gov/pubmed/34886766
VL  - 47
ER  -
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