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Development and validation of an interpretable machine learning model for cerebral small vessel disease risk
Tao Guo1, Mengchen Wang2, Changliang Wang1
1Medical Imaging Center, Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230026, China.
Objective:
Given the limited accessibility of magnetic resonance imaging (MRI) for diagnosing cerebral small vessel disease (CSVD) in community settings and the lack of practical early risk assessment tools, we aimed to develop and validate an interpretable machine learning (ML) model using non-imaging routine clinical data to identify individuals at high risk of CSVD and its imaging subgroups (lacunes and white matter hyperintensities [WMH]).
Methods:
Data were retrospectively collected from 1,489 participants in two community-based regions of China, with CSVD diagnosed by 0.35 T mobile MRI. Data were split into development and external validation cohorts. Recursive feature elimination (RFE) was employed for predictor selection, and five ML models (Logistic Regression, Support Vector Machine, Random Forest, eXtreme Gradient Boosting, Light Gradient Boosting Machine [LightGBM]) were developed and compared using nested five-fold cross-validation. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), accuracy, F1 score, and balanced accuracy. SHapley Additive exPlanations (SHAP) enhanced interpretability. The best-performing model was integrated with predictors selected through sequential forward selection (SFS) to build a web-based application.
Results:
The best-performing LightGBM model, evaluated across the outer folds of nested five-fold cross-validation, achieved an AUROC of 0.937 ± 0.003. In the test set, it demonstrated an AUROC of 0.862 (95 % CI 0.795-0.930). External validation in a geographically distinct region confirmed moderate generalizability with an AUROC of 0.761 (95 % CI 0.721-0.801). Among the 19 selected interpretable features, the top 10 features, age, uric acid, body mass index, creatinine, waist circumference, estimated glomerular filtration rate, fasting blood glucose, high-density lipoprotein cholesterol, homocysteine, and diastolic blood pressure, were integrated into a web-based calculator.
Conclusions:
This study developed a reliable and interpretable LightGBM model, based on routine clinical data, offering a practical CSVD screening tool for community healthcare. Its web-based deployment provides valuable support for cerebrovascular prevention and early intervention, addressing current MRI accessibility limitations.
