Machine learning-based prediction of longitudinal cognitive decline in early Parkinson's disease using multimodal features.

ABSTRACT: Patients with Parkinson's Disease (PD) often suffer from cognitive decline. Accurate prediction of cognitive decline is essential for early treatment of at-risk patients. The aim of this study was to develop and evaluate a multimodal machine learning model for the prediction of continuous cognitive decline in patients with early PD. We included 213 PD patients from the Parkinson's Progression Markers Initiative (PPMI) database. Machine learning was used to predict change in Montreal Cognitive Assessment (MoCA) score using the difference between baseline and 4-years follow-up data as outcome. Input features were categorized into four sets: clinical test scores, cerebrospinal fluid (CSF) biomarkers, brain volumes, and genetic variants. All combinations of input feature sets were added to a b

SUBMITTER: Almgren H 

PROVIDER: S-EPMC10425414 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature