Predicting executive functioning from walking features in Parkinson's disease using machine learning.

ABSTRACT: Parkinson's disease is characterized by motor and cognitive deficits. While previous work suggests a relationship between both, direct empirical evidence is scarce or inconclusive. Therefore, we examined the relationship between walking features and executive functioning in patients with Parkinson's disease using state-of-the-art machine learning approaches. A dataset of 103 geriatric Parkinson inpatients, who performed four walking conditions with varying difficulty levels depending on single task walking and additional motor and cognitive demands, was analyzed. Walking features were quantified using an inertial measurement unit (IMU) system positioned at the patient's lower back. The analyses included five imputation methods and four regression approaches to predict executive functioning

SUBMITTER: Piet A 

PROVIDER: S-EPMC11603322 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature