AUTHOR=McFall G. Peggy , Bohn Linzy , Gee Myrlene , Drouin Shannon M. , Fah Harrison , Han Wei , Li Liang , Camicioli Richard , Dixon Roger A. TITLE=Identifying key multi-modal predictors of incipient dementia in Parkinson’s disease: a machine learning analysis and Tree SHAP interpretation JOURNAL=Frontiers in Aging Neuroscience VOLUME=15 YEAR=2023 URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2023.1124232 DOI=10.3389/fnagi.2023.1124232 ISSN=1663-4365 ABSTRACT=Background

Persons with Parkinson’s disease (PD) differentially progress to cognitive impairment and dementia. With a 3-year longitudinal sample of initially non-demented PD patients measured on multiple dementia risk factors, we demonstrate that machine learning classifier algorithms can be combined with explainable artificial intelligence methods to identify and interpret leading predictors that discriminate those who later converted to dementia from those who did not.

Method

Participants were 48 well-characterized PD patients (Mbaseline age = 71.6; SD = 4.8; 44% female). We tested 38 multi-modal predictors from 10 domains (e.g., motor, cognitive) in a computationally competitive context to identify those that best discriminated two unobserved baseline groups, PD No Dementia (PDND), and PD Incipient Dementia (PDID). We used Random Forest (RF) classifier models for the discrimination goal and Tree SHapley Additive exPlanation (Tree SHAP) values for deep interpretation.

Results

An excellent RF model discriminated baseline PDID from PDND (AUC = 0.84; normalized Matthews Correlation Coefficient = 0.76). Tree SHAP showed that ten leading predictors of PDID accounted for 62.5% of the model, as well as their relative importance, direction, and magnitude (risk threshold). These predictors represented the motor (e.g., poorer gait), cognitive (e.g., slower Trail A), molecular (up-regulated metabolite panel), demographic (age), imaging (ventricular volume), and lifestyle (activities of daily living) domains.

Conclusion

Our data-driven protocol integrated RF classifier models and Tree SHAP applications to selectively identify and interpret early dementia risk factors in a well-characterized sample of initially non-demented persons with PD. Results indicate that leading dementia predictors derive from multiple complementary risk domains.