Abstract

This research presents a systematic deep-learning methodology for the early detection and severity classification of Parkinson's Disease (PD) using Hoehn and Yahr (HY) scores. The methodology comprises nine meticulously defined sub-sections, encompassing data acquisition, preprocessing, feature engineering, and model development. Central to the approach is the utilization of Convolutional Neural Networks (CNNs) implemented in the TensorFlow framework. Rigorous experimentation and evaluation ensure model performance optimization. A dedicated CNN architecture classifies HY scores, providing actionable insights into disease severity and facilitating the formulation of tailored treatment strategies. This investigation advances the detection and classification of PD through systematic exploration and application of cutting-edge deep learning. The proposed framework demonstrates promise for early diagnosis and personalized management of Parkinson's Disease, paving the way for future research aimed at improving patient outcomes. With a focus on leveraging state-of-the-art techniques, the methodology comprises nine meticulously crafted sub-sections, each delineating essential steps in the research process. Beginning with the strategic importation of libraries tailored to data handling and model development, the research progresses through data loading, visualization, preprocessing, and dataset partitioning for model training and testing. This research endeavors to advance the understanding and management of Parkinson's Disease through systematic experimentation, rigorous evaluation, and the application of cutting-edge deep learning techniques. By providing a comprehensive framework for PD detection and classification, this methodology lays the groundwork for future research endeavors aimed at improving early diagnosis and personalized patient care in Parkinson's Disease. This research delves into the intricate process of developing a machine-learning model designed for the detection and classification of gait freezing.