Applications of Neural Networks in Parkinson’s Disease Diagnosis

Parkinson's disease (PD) is a complex and debilitating neurodegenerative disorder that affects millions of people worldwide. Early and accurate diagnosis is crucial for effective treatment and management of PD. This thesis explores the application of neural networks in PD diagnosis, leveraging their ability to learn patterns from large datasets and make accurate predictions. Thesis provides an overview of PD, including its symptoms, diagnosis, and current challenges in diagnosis. We then delve into the fundamentals of neural networks, including supervised learning, mathematical interpretations, and parametric models. This research focuses on the development of neural network models that can accurately diagnose PD from various data sources, such as medical imaging, sensor data, and clinical features. We evaluate the performance of the models using various metrics and compare them with traditional diagnostic methods. The results demonstrate the potential of neural networks in improving the accuracy and efficiency of PD diagnosis, paving the way for early intervention and better patient outcomes. This research contributes to the growing body of literature on the application of artificial intelligence in healthcare, highlighting the promising role of neural networks in revolutionizing PD diagnosis.