Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly patients with cardiovascular metabolic diseases in China: a longitudinal study
Abstract Background The incidence of cardiovascular metabolic diseases (CMD) continues to rise among middle-aged and elderly populations, affecting not only physical health but also significantly increasing the risk of depression. This study aims to construct a machine learning model to predict the...
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2025-05-01
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| Online Access: | https://doi.org/10.1186/s12889-025-23075-7 |
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| author | Gege Zhang Sijie Dong Li Wang |
| author_facet | Gege Zhang Sijie Dong Li Wang |
| author_sort | Gege Zhang |
| collection | DOAJ |
| description | Abstract Background The incidence of cardiovascular metabolic diseases (CMD) continues to rise among middle-aged and elderly populations, affecting not only physical health but also significantly increasing the risk of depression. This study aims to construct a machine learning model to predict the risk of depression in middle-aged and elderly patients with CMD and to identssify key risk factors. Methods Based on data from the China Health and Retirement Longitudinal Study (CHARLS) from 2018 to 2020, 4,477 patients aged 45 and above were included. LASSO regression was used to screen for risk factors, and three machine learning algorithms—logistic regression (LR), random forest (RF), and XGBoost—were employed to build predictive models. The performance of the models was evaluated using ROC curves, calibration curves, and decision curves. Results The study found several risk factors significantly associated with depression, including disability status, pain, retirement status, number of chronic diseases, education level, age, gender, place of residence, life satisfaction, optimism about the future, and self-rated health status. The incidence of depression was significantly higher among women (56%), rural residents (64%), individuals with disabilities, non-retirees (85%), and those with chronic illnesses (73%). The LR model demonstrated the best predictive performance, with an AUC of 0.69. Key predictive factors included self-rated health, residence, education level, gender, pain, life satisfaction, age, and hope for the future. Conclusion This study developed a depression risk prediction model based on logistic regression, providing important references for psychological health interventions in middle-aged and elderly patients with CMD. Identifying and intervening in high-risk populations is crucial for improving patients' quality of life. |
| format | Article |
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| institution | DOAJ |
| issn | 1471-2458 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
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| spelling | doaj-art-00628cc0d625480bbb3d476072a6773f2025-08-20T03:08:40ZengBMCBMC Public Health1471-24582025-05-0125111810.1186/s12889-025-23075-7Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly patients with cardiovascular metabolic diseases in China: a longitudinal studyGege Zhang0Sijie Dong1Li Wang2Xuzhou Medical UniversityXuzhou Medical UniversityThe Affiliated Hospital of Xuzhou Medical UniversityAbstract Background The incidence of cardiovascular metabolic diseases (CMD) continues to rise among middle-aged and elderly populations, affecting not only physical health but also significantly increasing the risk of depression. This study aims to construct a machine learning model to predict the risk of depression in middle-aged and elderly patients with CMD and to identssify key risk factors. Methods Based on data from the China Health and Retirement Longitudinal Study (CHARLS) from 2018 to 2020, 4,477 patients aged 45 and above were included. LASSO regression was used to screen for risk factors, and three machine learning algorithms—logistic regression (LR), random forest (RF), and XGBoost—were employed to build predictive models. The performance of the models was evaluated using ROC curves, calibration curves, and decision curves. Results The study found several risk factors significantly associated with depression, including disability status, pain, retirement status, number of chronic diseases, education level, age, gender, place of residence, life satisfaction, optimism about the future, and self-rated health status. The incidence of depression was significantly higher among women (56%), rural residents (64%), individuals with disabilities, non-retirees (85%), and those with chronic illnesses (73%). The LR model demonstrated the best predictive performance, with an AUC of 0.69. Key predictive factors included self-rated health, residence, education level, gender, pain, life satisfaction, age, and hope for the future. Conclusion This study developed a depression risk prediction model based on logistic regression, providing important references for psychological health interventions in middle-aged and elderly patients with CMD. Identifying and intervening in high-risk populations is crucial for improving patients' quality of life.https://doi.org/10.1186/s12889-025-23075-7Cardiovascular metabolic diseasesDepressionMachine learningRisk predictionMiddle-aged and elderly population |
| spellingShingle | Gege Zhang Sijie Dong Li Wang Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly patients with cardiovascular metabolic diseases in China: a longitudinal study BMC Public Health Cardiovascular metabolic diseases Depression Machine learning Risk prediction Middle-aged and elderly population |
| title | Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly patients with cardiovascular metabolic diseases in China: a longitudinal study |
| title_full | Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly patients with cardiovascular metabolic diseases in China: a longitudinal study |
| title_fullStr | Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly patients with cardiovascular metabolic diseases in China: a longitudinal study |
| title_full_unstemmed | Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly patients with cardiovascular metabolic diseases in China: a longitudinal study |
| title_short | Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly patients with cardiovascular metabolic diseases in China: a longitudinal study |
| title_sort | construction of a machine learning based risk prediction model for depression in middle aged and elderly patients with cardiovascular metabolic diseases in china a longitudinal study |
| topic | Cardiovascular metabolic diseases Depression Machine learning Risk prediction Middle-aged and elderly population |
| url | https://doi.org/10.1186/s12889-025-23075-7 |
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