Early Detection of Parkinson's Disease using Advanced Neural Network

Abstract

Parkinson's disease is a progressive neurodegenerative disorder that often remains undetected in its initial stages due to subtle and easily overlooked symptoms. This condition affects the central nervous system, leading to a range of motor and non-motor symptoms. Recognizing the urgency of early diagnosis, we embarked on a comprehensive project aimed at developing a robust and efficient Parkinson's disease detection system. This paper focuses on leveraging cutting-edge machine learning algorithms to analyze a substantial and diverse dataset. By harnessing the power of advanced data processing techniques, we endeavour to unveil the intricate patterns and markers that signify the presence of Parkinson's disease in its nascent phases. This initiative aims to bridge the diagnostic gap, where conventional methods often fall short, enabling timely interventions and improved patient outcomes. Our study focused on utilizing advanced machine learning techniques to develop a Parkinson's disease detection model. By employing neural network algorithms, rigorous data preprocessing, and feature extraction from vocal data, our aim was to achieve accurate and early disease detection. The results obtained from our model are promising, demonstrating an accuracy of 83.0% and high recall and precision rates of 95.7% and 84.9%, respectively. The F1-score, a robust indicator of overall model performance, stood at an impressive 89.7%. These outcomes represent a significant advancement in Parkinson's disease detection, showcasing the potential of our model in real-world applications for early diagnosis. While further validation is required, our study lays a strong foundation for continued research, offering the prospect of improved healthcare outcomes for individuals affected by Parkinson's disease.