Interpretable Machine Learning for Cross-Cohort Prediction of Motor Fluctuations in Parkinson's Disease

Abstract

Background: Motor fluctuations are a common complication in later stages of Parkinson's disease (PD) and significantly affect patients' quality of life. Robustly identifying risk and protective factors for this complication across distinct cohorts could lead to improved disease management.

Objectives: The goal was to identify key prognostic factors for motor fluctuations in PD by using machine learning and exploring their associations in the context of the prior literature.

Methods: We applied interpretable machine learning techniques for time-to-event analysis and prediction of motor fluctuations within 4 years in three longitudinal PD cohorts. Prognostic models were cross-validated to identify robust predictors, and the performance, stability, calibration, and utility for clinical decision-making were assessed.

Results: Cross-validation analyses suggest the effectiveness of the models in identifying significant baseline predictors. Movement Disorder Society-Unified Parkinson's Disease Rating Scale parts I and II, freezing of gait, axial symptoms, rigidity, and pathogenic GBA and LRRK2 variants were positively correlated with motor fluctuations. Conversely, motor fluctuations were inversely associated with tremors and late age of onset of PD. Cross-cohort data integration provides more stable predictions, reducing cohort-specific bias and enhancing robustness. Decision curve and calibration analysis confirms the models' practical utility and alignment of predictions with observed outcomes.

Conclusions: Interpretable machine learning models can effectively predict motor fluctuations in PD from baseline clinical data. Cross-cohort data integration increases the stability of selected predictors. Calibration and decision curve analyses confirm the model's reliability and utility for practical clinical applications. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Keywords: cross‐cohort analysis; longitudinal cohorts; machine learning; motor fluctuations; predictive modeling.

FIG. 1

Illustration of the machine learning and cross‐validation workflow. The workflow involves training and evaluating machine learning models for motor fluctuation prognosis using 5‐fold cross‐validation to assess average performance. A 3‐fold nested cross‐validation on the training set data was used to optimize hyperparameters and select the most informative features. [Color figure can be viewed at wileyonlinelibrary.com]

FIG. 2

Shapley additive explanations (SHAP) value plot illustrating the top individual predictors for the best comprehensive model in cross‐cohort motor fluctuations classification. SHAP plot, providing a detailed view of how individual predictors influence the model's predictions. This visualization allows us to understand not just the overall importance of each predictor, but also how different values of that predictor impact the likelihood of motor fluctuations across our dataset. Each row represents a predictor, ordered by overall importance from top to bottom, and each point represents an individual observation in the dataset. The color of the points ranges from blue (lower values) to red (higher values) for that predictor. The position of points on the x‐axis shows the impact on the model's prediction: points to the right of zero indicate the predictor pushed the model toward predicting motor fluctuations, and points to the left of zero indicate the predictor pushed the model away from predicting motor fluctuations. Therefore, the SHAP plot shows whether high or low values of a predictor are associated with a higher or lower likelihood of predicted motor fluctuations. [Color figure can be viewed at wileyonlinelibrary.com]

FIG. 3

Shapley additive explanations (SHAP) value plot illustrating the most informative predictors for the best comprehensive model in the cross‐cohort time‐to‐motor fluctuation analysis. SHAP plot, providing a detailed view of how individual predictors influence the model's predictions. This visualization allows us to understand not just the overall importance of each predictor, but also how different values of that predictor impact the likelihood of motor fluctuations across our dataset. Each row represents a predictor, ordered by overall importance from top to bottom, and each point represents an individual observation in the dataset. The color of the points ranges from blue (lower values) to red (higher values) for that predictor. The position of points on the x‐axis shows the impact on the model's prediction: points to the right of zero indicate the predictor pushed the model toward predicting motor fluctuations, and points to the left of zero indicate the predictor pushed the model away from predicting motor fluctuations. Therefore, the SHAP plot shows whether high or low values of a predictor are associated with a higher or lower likelihood of predicted motor fluctuations. [Color figure can be viewed at wileyonlinelibrary.com]