Machine Learning-Based Early Detection of Parkinson’s Disease

Parkinson's disease (PD) is a neurological condition that worsens over time and has a major effect on quality of life and motor function. For better results and efficient care, early diagnosis is essential. Using clinical and biological speech data, this study suggests a machine learning-based method for the early identification of Parkinson's disease. The ability of many classification methods, such as Support Vector Machines (SVM), Random Forest, and k-Nearest Neighbors (k-NN), to differentiate between healthy people and PD patients was assessed. The model's promising sensitivity, specificity, and accuracy show promise as a non-invasive, affordable diagnostic tool. The findings demonstrate that incorporating machine learning methods into clinical procedures for the early diagnosis of Parkinson's disease is feasible