Analysis of Magnetic Resonance Imaging for Parkinson's Disease

This research paper explores the application of machine learning techniques for Parkinson's disease detection, aiming to enhance early diagnosis and personalized treatment strategies. By analysing diverse patient data, including clinical and imaging information, the study demonstrates the potential of machine learning in improving accuracy and efficiency in identifying Parkinson's disease patterns. We examined the effectiveness of the Support Vector Machine (SVM), Naive Bayes, K Nearest Neighbour (KNN), Logistic Regression, Decision Tree, Random Forest, and Perceptron models in detecting Parkinson’s Disease (PD) using MRI scan images. Our study demonstrates that KNN outperforms the other models, achieving an impressive accuracy rate of 94.87%. These findings underscore the potential of KNN as a robust tool for accurate Parkinson's disease detection, providing valuable insights for future diagnostic advancements.