Alternate fluency in Parkinson's disease: A machine learning analysis.

Objective: The aim of the present study was to investigate whether patients with Parkinson's Disease (PD) had changes in their level of performance in extra-dimensional shifting by implementing a novel analysis method, utilizing the new alternate phonemic/semantic fluency test.

Method: We used machine learning (ML) in order to develop high accuracy classification between PD patients with high and low scores in the alternate fluency test.

Results: The models developed resulted to be accurate in such classification in a range between 80% and 90%.