A Survey on Detection of Parkinson's Disease Through Clinical Data Using Deep Learning Approach

Parkinson's disease (PD) is a neurodegenerative disorder that affects millions globally, yet it is frequently diagnosed only in advanced stages due to reliance on subjective clinical assessments. This study explores the application of deep learning models specifically convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for the early detection of PD by analyzing multimodal clinical data, as well as voice recordings. Our model achieved high accuracy, precision, recall, and F1-score. This AI-driven approach demonstrates the feasibility of integrating advanced diagnostic tools into clinical workflows, offering an objective, scalable solution for earlier PD diagnosis and potentially improving patient outcomes through timely intervention.