ArticleFrontiers in psychiatry2024
Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly hypertensive people in China: a longitudinal study.
Article in Frontiers in psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Article
- An explainable machine learning model for predicting depressive symptoms among Chinese older adults with chronic diseases.BMC psychiatry · 2026Article
- A policy instrument-based textual analysis of elderly care policies in the context of low fertility in China.BMC health services research · 2026Article
- Dynamic nomogram for predicting depression risk in middle-aged and older adults based on CHARLS.Scientific reports · 2025Article
- Temporal trends and machine learning prediction of depressive symptoms among Chinese middle-aged and elderly individuals: a national cohort study.BMC public health · 2025Article
- Mobile Mental Health Screening in EmotiZen via the Novel Brain-Inspired MCoG-LDPSNet.Biomimetics (Basel, Switzerland) · 2025Article
- A machine learning-based approach to predict depression in Chinese older adults with subjective cognitive decline: a longitudinal study.Scientific reports · 2025Article
- Development and validation of a depression risk prediction model for rural elderly living alone.BMC psychiatry · 2025Article
- Predicting the risk of depression in older adults with disability using machine learning: an analysis based on CHARLS data.Frontiers in artificial intelligence · 2025Article
- Developing a suicide risk prediction model for hospitalized adolescents with depression in China.Frontiers in psychiatry · 2025Article
- Exploring the therapeutic effect of core components in Xuanshen Yishen mixture on hypertension through network pharmacology.American journal of translational research · 2024Article
- Article
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Abstract
Background: Hypertension is a common chronic disease that can trigger symptoms such as anxiety and depression. Therefore, it is essential to predict their risk of depression. The aim of this study is to find the best prediction model and provide effective intervention strategies for health professionals. Methods: The study subjects were 2733 middle-aged and older adults who participated in the China Health and Retirement Longitudinal Study (CHARLS) between 2018 and 2020. R software was used for Lasso regression analysis to screen the best predictor variables, and logistic regression, random forest and XGBoost models were constructed. Finally, the prediction efficiency of the three models was compared. Results: In this study, 18 variables were included, and LASSO regression screened out 10 variables that were important for the establishment of the model. Among the three models, Logistic Regression model showed the best performance in various evaluation indicators. Conclusion: The prediction model based on machine learning can accurately assess the likelihood of depression in middle-aged and elderly patients with hypertension in the next three years. And by combining Logistic regression and nomograms, we were able to provide a clear interpretation of personalized risk predictions.
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