Identification and validation of an explainable machine learning model for vascular depression diagnosis in the older adults: a multicenter cohort study
Abstract Background Vascular depression (VaDep) is a prevalent affective disorder in older adults that significantly impacts functional status and quality of life. Early identification and intervention are crucial but largely insufficient in clinical practice due to inconspicuous depressive symptoms...
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2025-07-01
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| Series: | BMC Medicine |
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| Online Access: | https://doi.org/10.1186/s12916-025-04283-9 |
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| author | Ran Zhang Tian Li Fan Fan Haoying He Liuyi Lan Dong Sun Zhipeng Xu Sisi Peng Jing Cao Juan Xu Xiaoxiang Peng Ming Lei Hao Song Junjian Zhang |
| author_facet | Ran Zhang Tian Li Fan Fan Haoying He Liuyi Lan Dong Sun Zhipeng Xu Sisi Peng Jing Cao Juan Xu Xiaoxiang Peng Ming Lei Hao Song Junjian Zhang |
| author_sort | Ran Zhang |
| collection | DOAJ |
| description | Abstract Background Vascular depression (VaDep) is a prevalent affective disorder in older adults that significantly impacts functional status and quality of life. Early identification and intervention are crucial but largely insufficient in clinical practice due to inconspicuous depressive symptoms mostly, heterogeneous imaging manifestations, and the lack of definitive peripheral biomarkers. This study aimed to develop and validate an interpretable machine learning (ML) model for VaDep to serve as a clinical support tool. Methods This study included 602 participants from Wuhan in China divided into 236 VaDep patients and 366 controls for training and internal validation from July 2020 to October 2023. An independent dataset of 171 participants from surrounding areas was used for external validation. We collected clinical data, neuropsychological assessments, blood test results, and MRI scans to develop and refine ML models through cross-validation. Feature reduction was implemented to simplify the models without compromising their performance, with validation achieved through internal and external datasets. The SHapley Additive exPlanations method was used to enhance model interpretability. Results The Light Gradient Boosting Machine (LGBM) model outperformed from the selected 6 ML algorithms based on performance metrics. An optimized, interpretable LGBM model with 8 key features, including white matter hyperintensities score, age, vascular endothelial growth factor, interleukin-6, brain-derived neurotrophic factor, tumor necrosis factor-alpha levels, lacune counts, and serotonin level, demonstrated high diagnostic accuracy in both internal (AUROC = 0.937) and external (AUROC = 0.896) validations. The final model also achieved, and marginally exceeded, clinician-level diagnostic performance. Conclusions Our research established a consistent and explainable ML framework for identifying VaDep in older adults, utilizing comprehensive clinical data. The 8 characteristics identified in the final LGBM model provide new insights for further exploration of VaDep mechanisms and emphasize the need for enhanced focus on early identification and intervention in this vulnerable group. More attention needs to be paid to the affective health of older adults. |
| format | Article |
| id | doaj-art-fd80e5dec5d449459de60db9b97e8f68 |
| institution | Kabale University |
| issn | 1741-7015 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medicine |
| spelling | doaj-art-fd80e5dec5d449459de60db9b97e8f682025-08-20T03:42:53ZengBMCBMC Medicine1741-70152025-07-0123111710.1186/s12916-025-04283-9Identification and validation of an explainable machine learning model for vascular depression diagnosis in the older adults: a multicenter cohort studyRan Zhang0Tian Li1Fan Fan2Haoying He3Liuyi Lan4Dong Sun5Zhipeng Xu6Sisi Peng7Jing Cao8Juan Xu9Xiaoxiang Peng10Ming Lei11Hao Song12Junjian Zhang13Department of Neurology, Zhongnan Hospital of Wuhan UniversityDepartment of Neurology, Zhongnan Hospital of Wuhan UniversityDepartment of Neurology, Zhongnan Hospital of Wuhan UniversityDepartment of Neurology, Zhongnan Hospital of Wuhan UniversityDepartment of Neurology, Zhongnan Hospital of Wuhan UniversityDepartment of Neurology, Zhongnan Hospital of Wuhan UniversityDepartment of Neuropsychology, Zhongnan Hospital of Wuhan UniversityDepartment of Neuropsychology, Zhongnan Hospital of Wuhan UniversityDepartment of Neurology, Zhongnan Hospital of Wuhan UniversityDepartment of Neurology, Zhongnan Hospital of Wuhan UniversityDepartment of Neurology, Third People’s Hospital of Hubei ProvinceDepartment of Neurology, General Hospital of the Yangtze River ShippingDepartment of Neurology, Zhongnan Hospital of Wuhan UniversityDepartment of Neurology, Zhongnan Hospital of Wuhan UniversityAbstract Background Vascular depression (VaDep) is a prevalent affective disorder in older adults that significantly impacts functional status and quality of life. Early identification and intervention are crucial but largely insufficient in clinical practice due to inconspicuous depressive symptoms mostly, heterogeneous imaging manifestations, and the lack of definitive peripheral biomarkers. This study aimed to develop and validate an interpretable machine learning (ML) model for VaDep to serve as a clinical support tool. Methods This study included 602 participants from Wuhan in China divided into 236 VaDep patients and 366 controls for training and internal validation from July 2020 to October 2023. An independent dataset of 171 participants from surrounding areas was used for external validation. We collected clinical data, neuropsychological assessments, blood test results, and MRI scans to develop and refine ML models through cross-validation. Feature reduction was implemented to simplify the models without compromising their performance, with validation achieved through internal and external datasets. The SHapley Additive exPlanations method was used to enhance model interpretability. Results The Light Gradient Boosting Machine (LGBM) model outperformed from the selected 6 ML algorithms based on performance metrics. An optimized, interpretable LGBM model with 8 key features, including white matter hyperintensities score, age, vascular endothelial growth factor, interleukin-6, brain-derived neurotrophic factor, tumor necrosis factor-alpha levels, lacune counts, and serotonin level, demonstrated high diagnostic accuracy in both internal (AUROC = 0.937) and external (AUROC = 0.896) validations. The final model also achieved, and marginally exceeded, clinician-level diagnostic performance. Conclusions Our research established a consistent and explainable ML framework for identifying VaDep in older adults, utilizing comprehensive clinical data. The 8 characteristics identified in the final LGBM model provide new insights for further exploration of VaDep mechanisms and emphasize the need for enhanced focus on early identification and intervention in this vulnerable group. More attention needs to be paid to the affective health of older adults.https://doi.org/10.1186/s12916-025-04283-9Vascular depressionLate-life depressionMachine learningLight Gradient Boosting MachineWhite matter hyperintensity |
| spellingShingle | Ran Zhang Tian Li Fan Fan Haoying He Liuyi Lan Dong Sun Zhipeng Xu Sisi Peng Jing Cao Juan Xu Xiaoxiang Peng Ming Lei Hao Song Junjian Zhang Identification and validation of an explainable machine learning model for vascular depression diagnosis in the older adults: a multicenter cohort study BMC Medicine Vascular depression Late-life depression Machine learning Light Gradient Boosting Machine White matter hyperintensity |
| title | Identification and validation of an explainable machine learning model for vascular depression diagnosis in the older adults: a multicenter cohort study |
| title_full | Identification and validation of an explainable machine learning model for vascular depression diagnosis in the older adults: a multicenter cohort study |
| title_fullStr | Identification and validation of an explainable machine learning model for vascular depression diagnosis in the older adults: a multicenter cohort study |
| title_full_unstemmed | Identification and validation of an explainable machine learning model for vascular depression diagnosis in the older adults: a multicenter cohort study |
| title_short | Identification and validation of an explainable machine learning model for vascular depression diagnosis in the older adults: a multicenter cohort study |
| title_sort | identification and validation of an explainable machine learning model for vascular depression diagnosis in the older adults a multicenter cohort study |
| topic | Vascular depression Late-life depression Machine learning Light Gradient Boosting Machine White matter hyperintensity |
| url | https://doi.org/10.1186/s12916-025-04283-9 |
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