Dataset Information

Prediction of the severity of obstructive sleep apnea by anthropometric features via support vector machine.

ABSTRACT: To develop an applicable prediction for obstructive sleep apnea (OSA) is still a challenge in clinical practice. We apply a modern machine learning method, the support vector machine to establish a predicting model for the severity of OSA. The support vector machine was applied to build up a prediction model based on three anthropometric features (neck circumference, waist circumference, and body mass index) and age on the first database. The established model was then valided independently on the second database. The anthropometric features and age were combined to generate powerful predictors for OSA. Following the common practice, we predict if a subject has the apnea-hypopnea index greater then 15 or not as well as 30 or not. Dividing by genders and age, for the AHI threhosld 15 (respectively 30), the cross validation and testing accuracy for the prediction were 85.3% and 76.7% (respectively 83.7% and 75.5%) in young female, while the negative likelihood ratio for the AHI threhosld 15 (respectively 30) for the cross validation and testing were 0.2 and 0.32 (respectively 0.06 and 0.1) in young female. The more accurate results with lower negative likelihood ratio in the younger patients, especially the female subgroup, reflect the potential of the proposed model for the screening purpose and the importance of approaching by different genders and the effects of aging.

SUBMITTER: Liu WT

PROVIDER: S-EPMC5417649 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Prediction of the severity of obstructive sleep apnea by anthropometric features via support vector machine.

Liu Wen-Te WT Wu Hau-Tieng HT Juang Jer-Nan JN Wisniewski Adam A Lee Hsin-Chien HC Wu Dean D Lo Yu-Lun YL

PloS one 20170504 5

To develop an applicable prediction for obstructive sleep apnea (OSA) is still a challenge in clinical practice. We apply a modern machine learning method, the support vector machine to establish a predicting model for the severity of OSA. The support vector machine was applied to build up a prediction model based on three anthropometric features (neck circumference, waist circumference, and body mass index) and age on the first database. The established model was then valided independently on t ...[more]

PMID: 28472141

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Project description:BackgroundObstructive sleep apnea (OSA) is a globally prevalent disease closely associated with hypertension. To date, no predictive model for OSA-related hypertension has been established. We aimed to use machine learning (ML) to construct a model to analyze risk factors and predict OSA-related hypertension.Materials and methodsWe retrospectively collected the clinical data of OSA patients diagnosed by polysomnography from October 2019 to December 2021 and randomly divided them into training and validation sets. A total of 1,493 OSA patients with 27 variables were included. Independent risk factors for the risk of OSA-related hypertension were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and the multilayer perceptron (MLP), were used to develop the model on the training set. The validation set was used to tune the model hyperparameters to determine the final prediction model. We compared the accuracy and discrimination of the models to identify the best machine learning algorithm for predicting OSA-related hypertension. In addition, a web-based tool was developed to promote its clinical application. We used permutation importance and Shapley additive explanations (SHAP) to determine the importance of the selected features and interpret the ML models.ResultsA total of 18 variables were selected for the models. The GBM model achieved the most extraordinary discriminatory ability (area under the receiver operating characteristic curve = 0.873, accuracy = 0.885, sensitivity = 0.713), and on the basis of this model, an online tool was built to help clinicians optimize OSA-related hypertension patient diagnosis. Finally, age, family history of hypertension, minimum arterial oxygen saturation, body mass index, and percentage of time of SaO2 < 90% were revealed by the SHAP method as the top five critical variables contributing to the diagnosis of OSA-related hypertension.ConclusionWe established a risk prediction model for OSA-related hypertension patients using the ML method and demonstrated that among the six ML models, the gradient boosting machine model performs best. This prediction model could help to identify high-risk OSA-related hypertension patients, provide early and individualized diagnoses and treatment plans, protect patients from the serious consequences of OSA-related hypertension, and minimize the burden on society.

Project description:Introduction: Obstructive sleep apnea (OSA) is a serious condition linked with various metabolic disorders and associated with increased all-cause and cardiovascular mortality. Although the potential mechanisms of pathophysiological processes related to OSA are relatively well known, the data regarding the correlation between obstructive sleep apnea, dyslipidemia, and systemic inflammation are still inconclusive. Methods: The study was conducted as a retrospective cohort study including 328 patients with newly diagnosed obstructive sleep apnea during the period between April 2018, and May 2020, in University Clinical Hospital Center "Bezanijska kosa", Belgrade, Serbia. Polysomnography was performed in all patients according to the protocol. Numerous demographic, antropometric, laboratory, and clinical data were correlated to Apnea-Hypopnea Index (AHI) as a dependent variable, with a particular review on the relation between lipid abnormalities, inflammatory parameters, and obstructive sleep apnea severity. Multivariate logistic regression model was used to assess predictors of severe OSA (AHI ≥30 per hour). Results: A total of 328 patients were included in the study. The mean age of the patients was 54.0 ± 12.5 years and more than two-thirds were male (68.8%). The majority of the patients had an AHI of at least 30 events per hour. Patients with severe OSA were more frequently male, obese, hypertensive and hyperlipidemic, and had increased neck circumference (both male and female patients). One hundred and thirty-two patients had metabolic syndrome. Patients with severe OSA more frequently had metabolic syndrome and significantly higher levels of glucose, creatinine, uric acid, AST, ALT, CK, microalbumine/creatinine ratio, triglyceride, total cholesterol, HDL, total cholеsterol to HDL-C ratio, CRP, and ESR. In the multivariate linear regression model with AHI (≥30 per hour) as a dependent variable, of demographic and clinical data, triglycerides ≥1.7 mmol/L and CRP >5 mg/L were significantly associated with AHI≥30 per hour. Conclusion: The present study on 328 patients with newly diagnosed obstructive sleep apnea revealed significant relation of lipid abnormalities, inflammatory markers, and other clinically important data with obstructive sleep apnea severity. These results can lead to a better understanding of the underlying pathophysiological processes and open the door to a new world of potentially useful therapeutic modalities.

Dataset Information

Prediction of the severity of obstructive sleep apnea by anthropometric features via support vector machine.

Publications

Prediction of the severity of obstructive sleep apnea by anthropometric features via support vector machine.

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