Abstract: Parkinson’s Disease (PD) is a progressive neurological disorder that affects movement and coordination, often diagnosed at later stages due to subtle early symptoms. Early and accurate detection of Parkinson’s Disease is crucial for timely intervention and improved quality of life. This project explores the application of machine learning techniques to detect Parkinson’s Disease using biomedical voice measurements and other relevant features. By training classifiers such as Support Vector Machines (SVM), Random Forest, and K-Nearest Neighbors (KNN) on datasets containing patient voice data and clinical attributes, the system learns to distinguish between healthy individuals and those with PD. Feature selection and data preprocessing are employed to enhance the model's accuracy and reduce overfitting. The results demonstrate that machine learning models can effectively support medical professionals in diagnosing Parkinson’s Disease, offering a non-invasive, cost-effective, and automated approach to early detection. This study highlights the potential of artificial intelligence in transforming traditional diagnostic processes in neurology.
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DOI:
10.17148/IJARCCE.2025.14656
