Detection of parkinson’s disease by using Machine Learning

This project proposes a machine learning-based approach to detect Parkinson’s Disease using Python. The proposed system utilizes a dataset of biomedical signals acquired from patients diagnosed with Parkinson’s Disease and healthy individuals. The dataset is preprocessed, and relevant features are extracted. Then, several machine-learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Networks (ANN), are trained on the extracted features. The performance of the trained models is evaluated using various metrics, including accuracy, sensitivity, specificity, and area under the curve (AUC). The results show that the proposed system achieves high accuracy in detecting Parkinson’s Disease using the biomedical signals. The system can potentially be used in clinical settings to aid physicians in the early detection of Parkinson’s Disease, leading to better patient outcomes. Parkinson’s Disease is a neurodegenerative disorder that affects millions of people worldwide. Early detection of Parkinson’s Disease is essential to prevent or delay its progression and to improve patient outcomes.