Can Statistical Machine Learning Algorithms Help for Classification of Obstructive Sleep Apnea Severity to Optimal Utilization of Polysomno graphy Resources?

Bozkurt, Selen; Bostancı, Aslı; Turhan, Murat

doi:10.3414/me16-01-0084

Cited by 19 publications

(7 citation statements)

References 33 publications

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“…They established SVM and RF OSA grading prediction models, achieving low performance with an AUC of 65, a sensitivity of 44.7, and an accuracy of 39.9 for SVM and an AUC of 63.7, a sensitivity of 44.1, and an accuracy of 44.1 for RF. Similarly, Bozkurt et al ( 2017 ) employed clinical data, including age, sex, BMI, neck circumference, smoking status, clinical symptoms, and physical examination, to build LR and RF four-category classification prediction models. The AUC for LR and RF was reported as 0.84 and 0.81, respectively, slightly outperforming the models established in this study.…”

Section: Discussionmentioning

confidence: 99%

Development and application of a machine learning-based predictive model for obstructive sleep apnea screening

Liu,

Geng,

Shen

et al. 2024

Front. Big Data

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ObjectiveTo develop a robust machine learning prediction model for the automatic screening and diagnosis of obstructive sleep apnea (OSA) using five advanced algorithms, namely Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) to provide substantial support for early clinical diagnosis and intervention.MethodsWe conducted a retrospective analysis of clinical data from 439 patients who underwent polysomnography at the Affiliated Hospital of Xuzhou Medical University between October 2019 and October 2022. Predictor variables such as demographic information [age, sex, height, weight, body mass index (BMI)], medical history, and Epworth Sleepiness Scale (ESS) were used. Univariate analysis was used to identify variables with significant differences, and the dataset was then divided into training and validation sets in a 4:1 ratio. The training set was established to predict OSA severity grading. The validation set was used to assess model performance using the area under the curve (AUC). Additionally, a separate analysis was conducted, categorizing the normal population as one group and patients with moderate-to-severe OSA as another. The same univariate analysis was applied, and the dataset was divided into training and validation sets in a 4:1 ratio. The training set was used to build a prediction model for screening moderate-to-severe OSA, while the validation set was used to verify the model's performance.ResultsAmong the four groups, the LightGBM model outperformed others, with the top five feature importance rankings of ESS total score, BMI, sex, hypertension, and gastroesophageal reflux (GERD), where Age, ESS total score and BMI played the most significant roles. In the dichotomous model, RF is the best performer of the five models respectively. The top five ranked feature importance of the best-performing RF models were ESS total score, BMI, GERD, age and Dry mouth, with ESS total score and BMI being particularly pivotal.ConclusionMachine learning-based prediction models for OSA disease grading and screening prove instrumental in the early identification of patients with moderate-to-severe OSA, revealing pertinent risk factors and facilitating timely interventions to counter pathological changes induced by OSA. Notably, ESS total score and BMI emerge as the most critical features for predicting OSA, emphasizing their significance in clinical assessments. The dataset will be publicly available on my Github.

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Section: Discussionmentioning

confidence: 99%

Development and application of a machine learning-based predictive model for obstructive sleep apnea screening

Liu,

Geng,

Shen

et al. 2024

Front. Big Data

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“…Although, PSG has the disadvantages of being laborious, time-consuming, and expensive. Therefore, many studies have been conducted to develop methods for screening OSAS without performing PSG, and the application of machine learning techniques has also been widely used 33 – 37 . In recent years, researches on the South Korean population have also been actively conducted.…”

Section: Discussionmentioning

confidence: 99%

Application of various machine learning techniques to predict obstructive sleep apnea syndrome severity

Han

2023

Sci Rep

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As the incidence of obstructive sleep apnea syndrome (OSAS) increases worldwide, the need for a new screening method that can compensate for the shortcomings of the traditional diagnostic method, polysomnography (PSG), is emerging. In this study, data from 4014 patients were used, and both supervised and unsupervised learning methods were used. Clustering was conducted with hierarchical agglomerative clustering, K-means, bisecting K-means algorithm, Gaussian mixture model, and feature engineering was carried out using both medically researched methods and machine learning techniques. For classification, we used gradient boost-based models such as XGBoost, LightGBM, CatBoost, and Random Forest to predict the severity of OSAS. The developed model showed high performance with 88%, 88%, and 91% of classification accuracy for three thresholds for the severity of OSAS: Apnea-Hypopnea Index (AHI) $$\ge $$ ≥ 5, AHI $$\ge $$ ≥ 15, and AHI $$\ge $$ ≥ 30, respectively. The results of this study demonstrate significant evidence of sufficient potential to utilize machine learning in predicting OSAS severity.

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“…Usually, a number of metrics for classification performance are calculated and evaluated, so the researcher can assess the accuracy of the predictive model. Recently, these methods are becoming more popular to help predict OSA diagnosis and severity using different types of clinical and anthropometric features (Liu et al 2017, Bozkurt et al 2017, Lee et al 2009). An important issue when applying supervised machine learning methods is the balance between accuracy (i.e.…”

Section: Novel Analytical Approaches Of Physiological Signals To Idenmentioning

confidence: 99%

Opportunities for utilizing polysomnography signals to characterize obstructive sleep apnea subtypes and severity

et al. 2018

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Can Statistical Machine Learning Algorithms Help for Classification of Obstructive Sleep Apnea Severity to Optimal Utilization of Polysomno graphy Resources?

Cited by 19 publications

References 33 publications

Development and application of a machine learning-based predictive model for obstructive sleep apnea screening

Development and application of a machine learning-based predictive model for obstructive sleep apnea screening

Application of various machine learning techniques to predict obstructive sleep apnea syndrome severity

Opportunities for utilizing polysomnography signals to characterize obstructive sleep apnea subtypes and severity

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