A machine learning model to predict the risk of depression in US adults with obstructive sleep apnea hypopnea syndrome
A cross-sectional study
Bibliographic Data
| ID | 22076293 |
|---|---|
| Authors | Enguang Li (0000-0002-0664-6449, Jinzhou Medical University), Fangzhu Ai (0009-0004-1615-1433, Jinzhou Medical University), Chunguang Liang (0000-0003-4493-6030, Jinzhou Medical University, corresponding author) |
| Year | 2024 |
| Volume | 11 |
| Pages | 1348803-1348803 |
| Publication date | 2024-01-08 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2023.1348803 |
| PMID | 38259742 |
| OpenAlex | W4390665076 |
| Language | EN |
| Citations received | 3 |
| References cited | 56 |
Objective: 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. 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. Study design: This is a cross-sectional study. Methods: 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. Results: 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. Conclusion: 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. By drawing the nomogram and applying it to the sleep testing center or sleep clinic, sleep technicians and medical staff can quickly and easily identify whether OSAHS patients have depression to carry out the necessary referral and psychological treatment
Apnea · Elastic net regularization · Environmental health · Feature selection · Hypopnea · Logistic regression · Machine learning · National Health and Nutrition Examination Survey · Nomogram · Obstructive sleep apnea · Polysomnography · Population · Random forest · Receiver operating characteristic · Sleep apnea · Statistics · Biological Research and Disease Studies · Computer Science · Mathematics · Medicine · Obstructive Sleep Apnea Research · Sleep and related disorders · Artificial Intelligence · Internal Medicine
Predictive Modeling of Comorbid Depression and Anxiety Symptoms Among Prospective University Students
Identifying major depressive disorder among US adults living alone using stacked ensemble machine learning algorithms
A Conceptual Framework to Understand the Relationships Between Digital Wellness and Artificial Intelligence
Depression, chronic diseases, and decrements in health
Time for united action on depression
No health without mental health
The PHQ-9
Retrospective Study on the Influencing Factors and Prediction of Hospitalization Expenses for Chronic Renal Failure in China Based on Random Forest and Lasso Regression
Machine learning model for depression based on heavy metals among aging people
Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer
Association of sleep apnea and depressive symptoms among US adults
Gender differences in depression in representative national samples
| Unique citing works | 3 |
|---|---|
| Citations per year | 3 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |