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Article

Towards Validating the Effectiveness of Obstructive Sleep Apnea Classification from Electronic Health Records Using Machine Learning

Department of Computer Science and Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates
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Author to whom correspondence should be addressed.
Academic Editor: Mahmudur Rahman
Healthcare 2021, 9(11), 1450; https://doi.org/10.3390/healthcare9111450
Received: 22 September 2021 / Revised: 23 October 2021 / Accepted: 25 October 2021 / Published: 27 October 2021
Obstructive sleep apnea (OSA) is a common, chronic, sleep-related breathing disorder characterized by partial or complete airway obstruction in sleep. The gold standard diagnosis method is polysomnography, which estimates disease severity through the Apnea-Hypopnea Index (AHI). However, this is expensive and not widely accessible to the public. For effective screening, this work implements machine learning algorithms for classification of OSA. The model is trained with routinely acquired clinical data of 1479 records from the Wisconsin Sleep Cohort dataset. Extracted features from the electronic health records include patient demographics, laboratory blood reports, physical measurements, habitual sleep history, comorbidities, and general health questionnaire scores. For distinguishing between OSA and non-OSA patients, feature selection methods reveal the primary important predictors as waist-to-height ratio, waist circumference, neck circumference, body-mass index, lipid accumulation product, excessive daytime sleepiness, daily snoring frequency and snoring volume. Optimal hyperparameters were selected using a hybrid tuning method consisting of Bayesian Optimization and Genetic Algorithms through a five-fold cross-validation strategy. Support vector machines achieved the highest evaluation scores with accuracy: 68.06%, sensitivity: 88.76%, specificity: 40.74%, F1-score: 75.96%, PPV: 66.36% and NPV: 73.33%. We conclude that routine clinical data can be useful in prioritization of patient referral for further sleep studies. View Full-Text
Keywords: electronic health records; machine learning; obstructive; polysomnography; prediction; sleep apnea electronic health records; machine learning; obstructive; polysomnography; prediction; sleep apnea
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MDPI and ACS Style

Ramesh, J.; Keeran, N.; Sagahyroon, A.; Aloul, F. Towards Validating the Effectiveness of Obstructive Sleep Apnea Classification from Electronic Health Records Using Machine Learning. Healthcare 2021, 9, 1450. https://doi.org/10.3390/healthcare9111450

AMA Style

Ramesh J, Keeran N, Sagahyroon A, Aloul F. Towards Validating the Effectiveness of Obstructive Sleep Apnea Classification from Electronic Health Records Using Machine Learning. Healthcare. 2021; 9(11):1450. https://doi.org/10.3390/healthcare9111450

Chicago/Turabian Style

Ramesh, Jayroop, Niha Keeran, Assim Sagahyroon, and Fadi Aloul. 2021. "Towards Validating the Effectiveness of Obstructive Sleep Apnea Classification from Electronic Health Records Using Machine Learning" Healthcare 9, no. 11: 1450. https://doi.org/10.3390/healthcare9111450

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