Machine Learning Technique for early Parkinson’s Disease Detection

Parkinson’s disease is a neurological ailment that affects millions of people globally and has serious health consequences. Neuro degenerative illnesses, notably Parkinson’s disease, presents substantial hurdles in clinical practice needs early detection. In this study, a thorough description of a new approach for Parkinson’s detection using Support Vector Machines (SVM) in the Google Colab computing environment is presented. The Programme developed tackles the important for PD early identification and treatment. The aim of this project is to create a sophisticated ML model that can identify Parkinson’s disease with accuracy by utilizing a variety of pertinent parameters. The basis of this study is a meticulously selected dataset from Kaggle. The results demonstrate the potential of the proposed SVM-based approach in improving the early diagnosis of Parkinson’s disease, highlighting its practical application in clinical settings.