A risk prediction system for depression in middle-aged and older adults grounded in machine learning and visualization technology: a cohort study
IntroductionMiddle-aged and older adults are highly susceptible to depression. For this reason, early identification and intervention can substantially reduce its prevalence. This study innovatively proposed a visual risk prediction system for depressive symptoms and depression in middle-aged and ol...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1606316/full |
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| author | Jinsong Du Jinsong Du Jinsong Du Xinru Tao Le Zhu Wenhao Qi Xiaoqiang Min Xiaoqiang Min Hongyan Deng Shujie Wei Xiaoyan Zhang Xiao Chang |
| author_facet | Jinsong Du Jinsong Du Jinsong Du Xinru Tao Le Zhu Wenhao Qi Xiaoqiang Min Xiaoqiang Min Hongyan Deng Shujie Wei Xiaoyan Zhang Xiao Chang |
| author_sort | Jinsong Du |
| collection | DOAJ |
| description | IntroductionMiddle-aged and older adults are highly susceptible to depression. For this reason, early identification and intervention can substantially reduce its prevalence. This study innovatively proposed a visual risk prediction system for depressive symptoms and depression in middle-aged and older adults, rooted in machine learning and visualization technologies.MethodsUsing cohort data from the China Health and Retirement Longitudinal Study (CHARLS), involving 8,839 middle-aged and older adult participants, the study developed predictive models based on eight machine learning algorithms, primarily including LightGBM, XGBoost, and AdaBoost. To enhance the interpretability of the XGBoost model, SHAP technology was employed to visualize the prediction results. The model was then deployed on a web platform to establish the risk prediction system.ResultsAmong the models, XGBoost demonstrated the best performance, achieving an average ROC-AUC of 0.69, and was ultimately selected as the predictive model for depressive symptoms and depression risk in this population. The developed risk prediction system can output the probability of users developing depressive symptoms or depression within five years and provide explanations for the prediction results, improving user accessibility and interpretability.DiscussionRooted in China's national longitudinal cohort, this platform integrates machine learning analytics with interactive visualization, with web deployment enhancing its clinical translational value. By enabling early depression detection and evidence-based interventions for middle-aged and older adult populations, it establishes a novel health management paradigm with demonstrated potential to improve quality of life. |
| format | Article |
| id | doaj-art-d3cd200fa10f46088fdca3114d02d66f |
| institution | OA Journals |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Public Health |
| spelling | doaj-art-d3cd200fa10f46088fdca3114d02d66f2025-08-20T02:03:04ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-06-011310.3389/fpubh.2025.16063161606316A risk prediction system for depression in middle-aged and older adults grounded in machine learning and visualization technology: a cohort studyJinsong Du0Jinsong Du1Jinsong Du2Xinru Tao3Le Zhu4Wenhao Qi5Xiaoqiang Min6Xiaoqiang Min7Hongyan Deng8Shujie Wei9Xiaoyan Zhang10Xiao Chang11School of Health Management, Zaozhuang University, Zaozhuang, ChinaSchool of Public Administration, Hangzhou Normal University, Hangzhou, ChinaDepartment of Teaching and Research, Shandong Coal Health School, Zaozhuang, ChinaSchool of Health Management, Zaozhuang University, Zaozhuang, ChinaSchool of Health Management, Zaozhuang University, Zaozhuang, ChinaSchool of Public Health and Nursing, Hangzhou Normal University, Hangzhou, ChinaDepartment of Teaching and Research, Shandong Coal Health School, Zaozhuang, ChinaDepartment of Geriatics, Shandong Healthcare Group Xinwen Central Hospital, Taian, ChinaSchool of Health Management, Zaozhuang University, Zaozhuang, ChinaImage Center, Zaozhuang Municipal Hospital, Zaozhuang, ChinaMagnetic Resonance Imaging Department, Shandong Healthcare Group Zaozhuang Central Hospital, Zaozhuang, ChinaSchool of Public Administration, Hangzhou Normal University, Hangzhou, ChinaIntroductionMiddle-aged and older adults are highly susceptible to depression. For this reason, early identification and intervention can substantially reduce its prevalence. This study innovatively proposed a visual risk prediction system for depressive symptoms and depression in middle-aged and older adults, rooted in machine learning and visualization technologies.MethodsUsing cohort data from the China Health and Retirement Longitudinal Study (CHARLS), involving 8,839 middle-aged and older adult participants, the study developed predictive models based on eight machine learning algorithms, primarily including LightGBM, XGBoost, and AdaBoost. To enhance the interpretability of the XGBoost model, SHAP technology was employed to visualize the prediction results. The model was then deployed on a web platform to establish the risk prediction system.ResultsAmong the models, XGBoost demonstrated the best performance, achieving an average ROC-AUC of 0.69, and was ultimately selected as the predictive model for depressive symptoms and depression risk in this population. The developed risk prediction system can output the probability of users developing depressive symptoms or depression within five years and provide explanations for the prediction results, improving user accessibility and interpretability.DiscussionRooted in China's national longitudinal cohort, this platform integrates machine learning analytics with interactive visualization, with web deployment enhancing its clinical translational value. By enabling early depression detection and evidence-based interventions for middle-aged and older adult populations, it establishes a novel health management paradigm with demonstrated potential to improve quality of life.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1606316/fulldepressionmachine learningCHARLSrisk predictionvisualization |
| spellingShingle | Jinsong Du Jinsong Du Jinsong Du Xinru Tao Le Zhu Wenhao Qi Xiaoqiang Min Xiaoqiang Min Hongyan Deng Shujie Wei Xiaoyan Zhang Xiao Chang A risk prediction system for depression in middle-aged and older adults grounded in machine learning and visualization technology: a cohort study Frontiers in Public Health depression machine learning CHARLS risk prediction visualization |
| title | A risk prediction system for depression in middle-aged and older adults grounded in machine learning and visualization technology: a cohort study |
| title_full | A risk prediction system for depression in middle-aged and older adults grounded in machine learning and visualization technology: a cohort study |
| title_fullStr | A risk prediction system for depression in middle-aged and older adults grounded in machine learning and visualization technology: a cohort study |
| title_full_unstemmed | A risk prediction system for depression in middle-aged and older adults grounded in machine learning and visualization technology: a cohort study |
| title_short | A risk prediction system for depression in middle-aged and older adults grounded in machine learning and visualization technology: a cohort study |
| title_sort | risk prediction system for depression in middle aged and older adults grounded in machine learning and visualization technology a cohort study |
| topic | depression machine learning CHARLS risk prediction visualization |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1606316/full |
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