Predicting cognitive impairment in Parkinson's disease: a machine learning approach based on clinical and...

Cognitive impairment is a common and disabling non-motor symptom of Parkinson's disease, markedly diminishing quality of life and elevating caregiver burden. Although considerable research has been conducted, the early prediction of cognitive impairment remains challenging owing to heterogeneous clinical presentations, variations in treatment adherence, and the inherent limitations in sensitivity of conventional biomarkers and cognitive assessment tools.

A retrospective cohort study involving 514 Parkinson's disease patients who had complete baseline data and a minimum of 6 months of follow-up. Participants were randomly allocated into a training cohort (n = 359) and a test cohort (n = 155). Demographic, clinical, biochemical, and neuropsychological variables were obtained at baseline. Cognitive impairment was defined based on Mini-Mental State Examination scores falling below education-adjusted thresholds and further validated using the Montreal Cognitive Assessment. Multiple machine learning models-including Random Forest, Logistic Regression, Gradient Boosting, CatBoost, and Support Vector Machine-were developed and evaluated using the area under the receiver operating characteristic curve, accuracy, recall, F1-score, calibration, and decision curve analysis. Feature importance analysis was performed to identify key predictive variables.

During follow-up, patients who developed cognitive impairment were significantly older and had longer disease duration, lower levels of albumin, hematocrit, and blood lipids, as well as a higher prevalence of hypertension. Feature selection identified: Age, Platelet count, Time from diagnosis to baseline visit, Apolipoprotein B, and Hematocrit as the predictors. The Random Forest model demonstrated the best overall performance, with the area under the receiver operating characteristic curve = 0.846, accuracy = 0.75, and an F1-score = 0.775, followed by CatBoost and Logistic Regression. Calibration and decision curve analyses confirmed stable probability estimation and superior clinical utility of Random Forest compared with "treat all" or "treat none" strategies. Further use the Montreal Cognitive Assessment score to verify the stability of the model.

Machine learning models integrating multimodal clinical and neuropsychological data demonstrate high accuracy in predicting cognitive impairment in Parkinson's disease, with Random Forest emerging as the most reliable approach. This framework provides a practical tool for early risk stratification, potentially enabling timely interventions and individualized management to reduce the burden of cognitive decline in Parkinson's disease.