Identification and validation of an explainable machine learning model for vascular depression diagnosis in the older adults: a multicenter cohort study.
Authors
Affiliations (6)
Affiliations (6)
- Department of Neurology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuchang District, Wuhan , Hubei Province, 430071, China.
- Department of Neuropsychology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province, China.
- Department of Neurology, Third People's Hospital of Hubei Province, Wuhan, Hubei Province, China.
- Department of Neurology, General Hospital of the Yangtze River Shipping, Wuhan, Hubei Province, China.
- Department of Neurology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuchang District, Wuhan , Hubei Province, 430071, China. [email protected].
- Department of Neurology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuchang District, Wuhan , Hubei Province, 430071, China. [email protected].
Abstract
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. 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. 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. 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.