Intelligent Parkinson's disease identification via Residual-Shuffle Network optimized by Improved Dandelion Optimizer

Parkinson's disease (PD) presents significant challenges due to its intricate symptoms and often delayed diagnosis. Therefore, early detection is vital for effective management and slowing the disease progression. Recently, machine learning based methods show high performance in this purpose. This research introduces a new machine learning approach that combines Residual-Shuffle Network (ResNet) with an advanced metaheuristic, the Improved Dandelion Optimizer (IDO) by integrating adaptive parameter control and enhanced exploration-exploitation balance, to provide an accessible and precise solution for automated PD detection. The proposed framework addresses previous limitations by effectively adjusting model hyperparameters and network weights without the need for expensive or sophisticated data collection devices. The proposed IDO-ResShuffle framework achieved strong performance on the HandPD dataset, obtaining 97.6% accuracy, 96.9% F1-score, 97.2% sensitivity, and 97.1% specificity. These results demonstrate the effectiveness of jointly optimizing the network architecture and hyperparameters through the Improved Dandelion Optimizer, enabling more reliable identification of Parkinson's disease from handwriting patterns. These enhancements empower healthcare professionals to make informed decisions about patient care sooner and potentially slow down the progression of symptoms. By enhancing accessibility and reliability, this approach can enhance clinical decision-making and support timely intervention in PD management.