Aphasia severity prediction using a multi-modal machine learning approach.

ABSTRACT: The present study examined an integrated multiple neuroimaging modality (T1 structural, Diffusion Tensor Imaging (DTI), and resting-state FMRI (rsFMRI)) to predict aphasia severity using Western Aphasia Battery-Revised Aphasia Quotient (WAB-R AQ) in 76 individuals with post-stroke aphasia. We employed Support Vector Regression (SVR) and Random Forest (RF) models with supervised feature selection and a stacked feature prediction approach. The SVR model outperformed RF, achieving an average root mean square error (RMSE) of 16.38±5.57, Pearson's correlation coefficient (r) of 0.70±0.13, and mean absolute error (MAE) of 12.67±3.27, compared to RF's RMSE of 18.41±4.34, r of 0.66±0.15, and MAE of 14.64±3.04. Resting-state neural activity and structural integrity emerged as crucial predictors of

SUBMITTER: Hu X 

PROVIDER: S-EPMC12261925 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature