Developing and validating a clinically actionable prediction tool for Parkinson disease using explainable machine learning with multidimensional national data: A cross-sectional study.

Parkinson disease (PD) is a rapidly growing neurodegenerative disorder that presents a significant public health challenge in aging societies, particularly in China. Although genetic, clinical, and environmental risk factors have been well-established, the predictive value of multidimensional factors (encompassing socioeconomic determinants, prodromal functional impairments, and lifestyle) remains underexplored in Chinese populations. We used data from the China Health and Retirement Longitudinal Study (n = 13,649; PD cases = 269) to develop and validate a machine learning prediction model for PD. Candidate predictors covered cognition, socioeconomic status, comorbidities, and lifestyle. Feature selection was performed using an ensemble approach (boruta, least absolute shrinkage and selection operator, and minimum redundancy maximum relevance), followed by extreme gradient boosting (XGBoost) model development with Bayesian hyperparameter optimization. Data were randomly split into a training set (70%) and a validation set (30%) for internal validation. Model performance was evaluated using area under the receiver operating characteristic curve, decision curve analysis, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. Model interpretability was evaluated using SHapley Additive exPlanations. Key predictors included cognitive impairment (memory and executive deficits), physical inactivity (low metabolic-equivalent scores), socioeconomic indicators (retirement status and financial support), educational attainment, and comorbidities (e.g., liver disease and wrist pain). In the validation set, the optimized XGBoost model achieved an area under the curve of 0.967 (95% confidence interval 0.962-0.972), sensitivity 82.0%, specificity 94.7%, positive predictive value 81.5%, negative predictive value 94.9%, and F1-score 81.7. SHapley Additive exPlanations analyses supported the consistency and relative importance of these predictors. An explainable XGBoost model integrating multidimensional national data showed excellent internal validation performance for identifying individuals with PD in China Health and Retirement Longitudinal Study. This tool may help support PD risk stratification in Chinese populations; external validation and prospective evaluation are warranted before clinical implementation.