Explainable machine learning identifies predictors for postural instability and cognitive impairment in early Parkinsons Disease

Parkinsons disease (PD) exhibits considerable variability in symptom onset, severity, and clinical trajectories, making prediction of individual disease progression a complex challenge. Leveraging large multimodal datasets from two independent cohorts of early PD patients, we examined predictive features for progression to mild cognitive impairment (MCI) and postural instability (PI) using Explainable Boosting Machines (EBM) - an interpretable machine learning (ML) approach. We found comparable predictive performances of EBM models using a minimal clinico-demographic dataset compared to an ext

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