A Multi-Modal Approach for Early and Accurate Diagnosis of Parkinson's Disease Using Advanced Diagnostic Techniques

Parkinson's disease (PD) is a prevalent neurological disorder characterized by motor symptoms such as tremors, bradykinesia, and stiffness, necessitating accurate and early diagnosis for effective management. Despite advancements, existing diagnostic methods often lack precision and reliability. This study addresses the gap by proposing a novel multimodal integration approach utilizing speech and handwriting data, coupled with machine learning techniques, to enhance diagnostic accuracy. The methodology involves preprocessing and feature extraction from voice and handwriting datasets, followed by the application of various machine learning algorithms for classification and regression tasks. Late fusion techniques, such as weighted averaging, are employed to combine results, improving overall diagnostic performance. Preliminary findings demonstrate the potential of this approach in providing a reliable tool for early detection of PD, with significant implications for clinical practice and patient care.