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:2022:10.3390/s22228630,
author = {Tsai, C-Y and Huang, H-T and Cheng, H-C and Wang, J and Duh, P-J and Hsu, W-H and Stettler, M and Kuan, Y-C and Lin, Y-T and Hsu, C-R and Lee, K-Y and Kang, J-H and Wu, D and Lee, H-C and Wu, C-J and Majumdar, A and Liu, W-T},
doi = {10.3390/s22228630},
journal = {Sensors (Basel, Switzerland)},
pages = {1--15},
title = {Screening for obstructive sleep apnea risk by using machine learning approaches and anthropometric features.},
url = {http://dx.doi.org/10.3390/s22228630},
volume = {22},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Obstructive sleep apnea (OSA) is a global health concern and is typically diagnosed using in-laboratory polysomnography (PSG). However, PSG is highly time-consuming and labor-intensive. We, therefore, developed machine learning models based on easily accessed anthropometric features to screen for the risk of moderate to severe and severe OSA. We enrolled 3503 patients from Taiwan and determined their PSG parameters and anthropometric features. Subsequently, we compared the mean values among patients with different OSA severity and considered correlations among all participants. We developed models based on the following machine learning approaches: logistic regression, k-nearest neighbors, naïve Bayes, random forest (RF), support vector machine, and XGBoost. Collected data were first independently split into two data sets (training and validation: 80%; testing: 20%). Thereafter, we adopted the model with the highest accuracy in the training and validation stage to predict the testing set. We explored the importance of each feature in the OSA risk screening by calculating the Shapley values of each input variable. The RF model achieved the highest accuracy for moderate to severe (84.74%) and severe (72.61%) OSA. The level of visceral fat was found to be a predominant feature in the risk screening models of OSA with the aforementioned levels of severity. Our machine learning models can be employed to screen for OSA risk in the populations in Taiwan and in those with similar craniofacial structures.
AU  - Tsai,C-Y
AU  - Huang,H-T
AU  - Cheng,H-C
AU  - Wang,J
AU  - Duh,P-J
AU  - Hsu,W-H
AU  - Stettler,M
AU  - Kuan,Y-C
AU  - Lin,Y-T
AU  - Hsu,C-R
AU  - Lee,K-Y
AU  - Kang,J-H
AU  - Wu,D
AU  - Lee,H-C
AU  - Wu,C-J
AU  - Majumdar,A
AU  - Liu,W-T
DO  - 10.3390/s22228630
EP  - 15
PY  - 2022///
SN  - 1424-8220
SP  - 1
TI  - Screening for obstructive sleep apnea risk by using machine learning approaches and anthropometric features.
T2  - Sensors (Basel, Switzerland)
UR  - http://dx.doi.org/10.3390/s22228630
UR  - https://www.ncbi.nlm.nih.gov/pubmed/36433227
UR  - https://www.mdpi.com/1424-8220/22/22/8630
UR  - http://hdl.handle.net/10044/1/100865
VL  - 22
ER  -
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