Keywords: Diagnosis/Prediction, Diagnosis/Prediction, Cerebral Small Vessel Disease
Motivation: The diagnosis of cerebral small vessel disease (CSVD) primarily relies on magnetic resonance imaging (MRI); however, its relatively high cost poses a challenge for implementing CSVD screening in the general population, particularly in low- and middle-income countries.
Goal(s): To detect CSVD in the general population using routine health examination data.
Approach: We developed and validated a machine learning (ML) model within a novel framework, termed Risk Assessment of CSVD in the General Population (RACGP).
Results: The LightGBM model based on RACGP achieved area under the curve (AUC) values of 0.862 on the test set and 0.789 on the external validation set.
Impact: Our ML model can identify CSVD patients within health examination populations in a low-cost manner, showing potential for CSVD screening.
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