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. 2021 Oct 27;9(11):1450.
doi: 10.3390/healthcare9111450.

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

Jayroop Ramesh  1 , Niha Keeran  1 , Assim Sagahyroon  1 , Fadi Aloul  1
Affiliations

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

Jayroop Ramesh et al. Healthcare (Basel). .

Abstract

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.

Keywords: electronic health records; machine learning; obstructive; polysomnography; prediction; sleep apnea.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
High level view of the proposed methodology.
Figure 2
Figure 2
Clinical features ordered as per Pearson’s Correlation Coefficient.
Figure 3
Figure 3
Clinical features ordered as per Kendall’s Tau.
Figure 4
Figure 4
Clinical features ordered as per Extremely Randomized Trees.
Figure 5
Figure 5
Clinical features ordered as per Mutual Information.
Figure 6
Figure 6
PSG features ordered as per Pearson’s Correlation Coefficient.
Figure 7
Figure 7
PSG features ordered as per Extremely Randomized Trees.
Figure 8
Figure 8
PSG features ordered as per Mutual Information.
See this image and copyright information in PMC

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