Machine Learning for Parkinson’s Disease Progression Prediction Using Gait Data and Neuroimaging Features

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder characterized by motor dysfunction and cognitive decline, necessitating robust predictive models for early diagnosis and disease monitoring. In recent years, the integration of machine learning (ML) techniques into clinical neurology has demonstrated promising potential for enhancing the prediction of disease progression. This study explores a multimodal machine learning framework that leverages gait dynamics and neuroimaging features to forecast the progression trajectory of Parkinson’s Disease. Gait data, extracted through wearable inertial sensors and pressure-sensitive walkways, provide real-time motor function assessments, while structural and functional magnetic resonance imaging (MRI/fMRI) deliver rich neuroanatomical and connectivity markers correlated with disease severity. The proposed model employs feature selection strategies such as recursive feature elimination and principal component analysis to reduce dimensionality and enhance model interpretability. Multiple supervised learning algorithms—including support vector machines (SVM), random forest (RF), and deep neural networks (DNN)—are trained and evaluated on a clinically validated dataset comprising longitudinal gait and imaging data. Performance metrics including accuracy, area under the receiver operating characteristic curve (AUC-ROC), and mean absolute error (MAE) are employed to compare model effectiveness across different stages of PD, as defined by the Hoehn and Yahr scale and Unified Parkinson's Disease Rating Scale (UPDRS). Findings suggest that multimodal models outperform unimodal counterparts, with the combination of gait variability indices and cortical thinning patterns showing the strongest predictive capability. The study underscores the value of integrating heterogeneous data sources in machine learning pipelines for clinical prognosis. Furthermore, this research contributes to the development of precision medicine approaches, enabling personalized therapeutic interventions and optimized disease management strategies for Parkinson’s patients.