Multimodal Machine Learning Approach for Predicting Cognitive Decline in People with Parkinson’s Disease

This study developed machine learning models to predict cognitive decline in individuals with Parkinson’s disease (PD) by integrating clinical characteristics and gait-derived digital biomarkers. Using data from 102 patients diagnosed with PD, we trained least absolute shrinkage and selection operator regression and eXtreme Gradient Boosting models on multimodal features, including clinical characteristics, physical function, lifestyle factors, and gait-derived features. Key predictors included Mini-Mental State Examination scores and gait biomarkers such as stride length of the left foot during preferred speed leftward turning, maximum acceleration of the right ankle during faster speed leftward turning, maximum jerk of the right ankle during forward walking, and the maximum gyroscope at the posterior superior iliac spine during forward walking. Stepwise regression explained 61.7% of the variance in Montreal Cognitive Assessment scores (p < 0.05). A logistic regression classifier using ten selected features achieved 76.5% accuracy and an area under the curve of 0.895 in identifying individuals with cognitive decline. These findings suggest that combining standard cognitive assessments with quantitative gait analysis enhances prediction and classification of cognitive impairment in PD, offering a clinically applicable strategy.