Hybrid models for handwriting-based diagnosis of Parkinson's disease

Abstract

Parkinson's disease (PD) is a progressive neurodegenerative disorder that primarily impairs motor functions, leading to symptoms such as tremors and micrographia. Even though early identification of PD is crucial for effective intervention, existing methods of diagnosis are highly invasive and not very sensitive to the early stages of the diseases. The goal of this research is to determine if handwriting could be a non-invasive way to diagnose PD at an early stage. We employed a dataset of 3,264 hand-drawn waves and spirals to evaluate the performance of hybrid machine learning and deep learning models which included Support Vector Machine (SVM), Random Forest (RF), Visual Geometry Group-16 (VGG-16) and MobileNetV2. Combing SVM with VGG-16 for the task reached a stunning 99.00% accuracy for identifying PD, performing the best out of all tested models, demonstrating superior performance in the early identification of PD. The proposed approach not only outperforms existing diagnostic methods but also underscores the transformative potential of handwriting analysis tools in PD diagnosis, aiding in automatic PD detection and enhancing patient outcomes.