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%. 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%. Following the removal of the semantic fluency test from the system, the parameter which best contributed to the classification was the phonemic fluency test. The best cut-offs were identified and the decision rule yielded an overall accuracy of 80.43%. Lastly, in order to evaluate the classification accuracy based on the shifting index, the best cut-offs based on an optimal single rule yielded an overall accuracy of 83.69%. Conclusion: We found that ML analysis of semantic and phonemic verbal fluency may be used to identify simple rules with high accuracy and good out of sample generalization, allowing the detection of executive deficits in patients with PD.