A coarse-to-fine feature selection method for accurate detection of cerebral small vessel disease

Cerebral small vessel disease (SVD) is common in the elderly and is associated with loss of functional independence, institutionalization, and death. In this paper, we propose a coarse-to-fine feature selection method for accurate SVD detection and timely implementation of interventions. The proposed method first uses an Iterative Random Forest based Feature Selection (IRFFS) method to obtain the most representative features from a feature set that includes gait, balance, and agility performance features extracted from 17 predefined clinical actions. The method then uses the Feature Incremental Extreme Learning Machine (FIELM) model to further verify the discriminant ability of each kind of selected features. Our results demonstrate that the proposed method can effectively select the most significant features for SVD detection, which include gait and agility performance features. Our method achieves up to 91.44% classification accuracy, outperforming other state-of-the-art feature selection methods. Our findings also verify clinical observations indicating that the fine motor pattern features of upper and lower limbs are helpful for high-accuracy SVD detection.