A Comparative Framework Integrating Hybrid Convolutional and Unified Graph Neural Networks for Accurate Parkinson’s Disease Classification

Parkinson’s disease (PD) is a progressive neurodegenerative disorder, affects motor function and is often challenging to diagnose due to the complex interplay of clinical features. This study integrates a comparative framework integrating hybrid Convolutional Neural Networks (PCNN) and graph-based models (GCN, GAT) to enhance Parkinson’s disease (PD) diagnosis using structured medical data. PD, a progressive neurodegenerative disorder affecting motor function, poses diagnostic challenges due to complex clinical feature interactions. The PCNN employs 1D convolutions to capture local feature patterns, while GCN and GAT model intricate interdependencies between clinical variables by representing the dataset as a graph. Notably, GAT’s attention mechanism dynamically prioritizes important features, improving interpretability and diagnostic precision. Through hyperparameter optimization with Optuna and addressing class imbalance using SMOTE, our approach achieved a peak accuracy of 97.44%, surpassing traditional methods. The comparative analysis reveals that while PCNN excels in classification accuracy, GAT’s attention-based feature selection offers superior interpretability. This makes it a valuable tool for more precise Parkinson’s disease detection in clinical applications. The integration of these models provides a comprehensive framework for PD diagnosis, leveraging both local and global feature extraction techniques. This study represents a significant advancement in applying advanced machine learning to neurodegenerative disease diagnostics, offering improved early detection and personalized treatment potential for Parkinson’s disease.