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

ABSTRACT:

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%. The predictor which demonstrated maximum efficiency in classifying the participants as low or high performers was the semantic fluency test. The optimal cut-off of a decision rule based on this test yielded an accuracy of 86.96%.

SUBMITTER: Ferrucci R 

PROVIDER: S-EPMC8942276 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature