Identifying network state-based Parkinson's disease subtypes using clustering and support vector machine models.

ABSTRACT:

Introduction

Parkinson's disease (PD) heterogeneity poses challenges to the current development of discovering the best therapeutic targets.

Methods

Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson's Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. The features were specifically the gray matter volume and dopaminergic features of the neostriatum, i.e., the caudate, putamen, and anterior putamen. We use machine learning (ML) algorithms, including Random Forest, Logistic Regression, and Support Vector Machine, to evaluate the diagnostic power of the brain features and network patterns in differentiating the PD sub

SUBMITTER: Nguchu BA 

PROVIDER: S-EPMC11865070 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature