Artificial Intelligence Assisted Classifier’s and Neural Network Based Prediction and Classification of Parkinson’s Disease

Parkinson's illness inhibits movement. Early diagnosis is essential for effective treatment. Neural networks and machine learning algorithms are very adept at processing large amounts of data, finding patterns, and producing precise predictions. AI has made medical diagnosis and illness prognosis better, particularly for Parkinson's disease. Artificial intelligence (AI)-driven classifiers and neural network-based prediction systems for Parkinson's disease early detection and staging are two noteworthy applications. As new data is added, these systems can adapt and improve, making them valuable for medical research. Neural network-based Parkinson's disease prediction models have shown promise. These algorithms can analyse patient biomarkers, medical history, and demographics to predict disease development or progression. Thanks to their ability to find complex data connections, neural networks can make personalized predictions and improve early intervention and therapy. AI improves the accuracy, speed of diagnosis and prognosis of Parkinson's disease. Doctors need an accurate diagnosis to quickly initiate the appropriate medications and support programs. Neural network-based prediction models and AI-powered classifiers show promise in treating Parkinson's disease, but AI experts and doctors need to work together. To guarantee the dependability and generalizability of these cutting-edge techniques in clinical practice, data protection, ethical AI usage, and model validation across many patient groups are required. In order to correctly portray Parkinson's illness, the research suggests using five significant classifiers: logistic regression, support vector machines, decision trees, random forests, closest neighbours, and effective sequential neural networks. Among the five classifiers, K-Nearest Neighbour had the highest accuracy rate of 94%, while the sequential neural network predicted an accuracy rate of 90%.