Association of healthy lifestyle with risk of obstructive sleep apnea: a cross-sectional study

Duan, Xueru; Huang, Jun; Zheng, Murui; Zhao, Wenjing; Lao, Lixian; Li, Haiyi; Wang, Zhiwei; Lu, Jiahai; Chen, Weiqing; Deng, Hai; Li, Xudong

doi:10.1186/s12890-021-01818-7

Cited by 18 publications

(11 citation statements)

References 48 publications

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“…Lifestyle modifications, such as smoking cessation and alcohol moderation, are essential components of sleep apnea management ( Duan et al, 2022 ). Additionally, addressing medical conditions and nasal congestion may lead to improved sleep apnea outcomes ( Paidi et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review of Risk factors for Sleep Apnea

Yayan,

Rasche

2024

Preventive Medicine Reports

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Section: Discussionmentioning

confidence: 99%

A Systematic Review of Risk factors for Sleep Apnea

Yayan,

Rasche

2024

Preventive Medicine Reports

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“…More precisely, some features such as craniofacial features, that vary among different ethnicities, may affect the AHI and OSA severity. 58 Other lifestyle habits, such as tobacco and alcohol usage 59 or one's personal health status (menopausal status, medication use, and comorbidities), 60 , 61 may also affect OSA severity. Some researchers proposed machine learning approaches by employing nocturnal oxygen saturation obtained by pulse oximetry and electrocardiogram signals for screening for OSA risk.…”

Section: Discussionmentioning

confidence: 99%

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

et al. 2023

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Objectives Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features. Methods We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. Results The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk. Conclusions The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.

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“…Although we excluded patients with low sleep efficiency, further works may consider using data from repeated PSG to prevent or reduce such bias [47]. Fourth, this retrospective study lacked information regarding lifestyle habits (tobacco and alcohol use) [48] or personal health status (menopausal status and comorbidities) [49,50]. Nevertheless, the association between OSA and these baseline details has been documented.…”

Section: Discussionmentioning

confidence: 99%

Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features

Tsai

Huang

Cheng

et al. 2022

Sensors

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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.

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Association of healthy lifestyle with risk of obstructive sleep apnea: a cross-sectional study

Cited by 18 publications

References 48 publications

A Systematic Review of Risk factors for Sleep Apnea

A Systematic Review of Risk factors for Sleep Apnea

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features

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