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. 2022 Mar 23;17(3):e0265803.
doi: 10.1371/journal.pone.0265803. eCollection 2022.

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

Roberta Ferrucci  1   2   3 , Francesca Mameli  3 , Fabiana Ruggiero  3 , Mariella Reitano  2 , Mario Miccoli  4 , Angelo Gemignani  5 , Ciro Conversano  5 , Michelangelo Dini  1 , Stefano Zago  3 , Silvie Piacentini  6 , Barbara Poletti  7 , Alberto Priori  1   2 , Graziella Orrù  5
Affiliations

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

Roberta Ferrucci et al. PLoS One. .

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%. 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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Correlations between age and fluency tests scores.
Age and phonemic fluency (r = −0.31, p = 0.0002); age and semantic fluency (r = −0.3, p = 0.0003); age and alternate fluency (r = −0.32, p < 0.0001). PF, Phonemic Fluency; SF, Semantic Fluency; AF, Alternate Fluency.
Fig 2
Fig 2. Correlations between age and alternate fluency with the sample stratified for MMSE or MOCA.
AF/MMSE: r = −0.28, p = 0.04; AF/MOCA: r = −0.35, p = 0.001. AF, Alternate Fluency; MMSE, Mini Mental State Examination; MOCA, Montreal Cognitive Assessment.
Fig 3
Fig 3. Correlations between age and alternate fluency with the sample stratified for gender.
AF/females: r = −0.53, p = 0.0002; AF/males: r = −0.2, p = 0.38. AF, Alternate Fluency; F, Females; M, Males.
Fig 4
Fig 4. Correlations between age and shifting index in the sample stratified for gender.
The shifting index is significant for females alone (r = −0.44, p = 0.003). F, Females; M, Males; SHIFT, shifting index.
See this image and copyright information in PMC

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