Sex-Specific Imaging Biomarkers for Parkinson's Disease Diagnosis: A Machine Learning Analysis.

ABSTRACT: This study aimed to identify sex-specific imaging biomarkers for Parkinson's disease (PD) based on multiple MRI morphological features by using machine learning methods. Participants were categorized into female and male subgroups, and various structural morphological features were extracted. An ensemble Lasso (EnLasso) method was employed to identify a stable optimal feature subset for each sex-based subgroup. Eight typical classifiers were adopted to construct classification models for PD and HC, respectively, to validate whether models specific to sex subgroups could bolster the precision of PD identification. Finally, statistical analysis and correlation tests were carried out on significant brain region features to identify potential sex-specific imaging biomarkers. The best model (ML

SUBMITTER: Yang Y 

PROVIDER: S-EPMC11950501 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature