首页|Jinzhou Medical University Reports Findings in Obstructive Sleep Apnea (A machine learning model to predict the risk of depression in US adults with obstructive sleep apnea hypopnea syndrome: a cross-sectional study)
Jinzhou Medical University Reports Findings in Obstructive Sleep Apnea (A machine learning model to predict the risk of depression in US adults with obstructive sleep apnea hypopnea syndrome: a cross-sectional study)
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New research on Respiratory Tract Diseases and Conditions - Obstructive Sleep Apnea is the subject of a report. According to news reporting originating from Jinzhou, People’s Republic of China, by NewsRx correspondents, research stated, “Depression is very common and harmful in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). It is necessary to screen OSAHS patients for depression early.” Our news editors obtained a quote from the research from Jinzhou Medical University, “However, there are no validated tools to assess the likelihood of depression in patients with OSAHS. This study used data from the National Health and Nutrition Examination Survey (NHANES) database and machine learning (ML) methods to construct a risk prediction model for depression, aiming to predict the probability of depression in the OSAHS population. Relevant features were analyzed and a nomogram was drawn to visually predict and easily estimate the risk of depression according to the best performing model. This is a cross-sectional study. Data from three cycles (2005-2006, 2007-2008, and 2015-2016) were selected from the NHANES database, and 16 influencing factors were screened and included. Three prediction models were established by the logistic regression algorithm, least absolute shrinkage and selection operator (LASSO) algorithm, and random forest algorithm, respectively. The receiver operating characteristic (ROC) area under the curve (AUC), specificity, sensitivity, and decision curve analysis (DCA) were used to assess evaluate and compare the different ML models. The logistic regression model had lower sensitivity than the lasso model, while the specificity and AUC area were higher than the random forest and lasso models. Moreover, when the threshold probability range was 0.19-0.25 and 0.45-0.82, the net benefit of the logistic regression model was the largest. The logistic regression model clarified the factors contributing to depression, including gender, general health condition, body mass index (BMI), smoking, OSAHS severity, age, education level, ratio of family income to poverty (PIR), and asthma. This study developed three machine learning (ML) models (logistic regression model, lasso model, and random forest model) using the NHANES database to predict depression and identify influencing factors among OSAHS patients. Among them, the logistic regression model was superior to the lasso and random forest models in overall prediction performance.”
JinzhouPeople’s Republic of ChinaAsiaAlgorithmsApneaCraniofacialCyborgsEmerging TechnologiesHealth and MedicineHypopnea SyndromeMachine LearningObstructive Sleep ApneaOtolaryngologyPulmonologyRespiration DisordersRespiratory Tract Diseases and ConditionsSleep ApneaSleep Diseases and ConditionsSleep Disorders
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