Speech Based Early Detection of Parkinson's Disease

The paper presents a comprehensive review of the current methodologies for early-stage Parkinson's Disease (PD) identification, categorizing them into clinical assessments, neuroimaging techniques, and machine learning-based approaches. Clinical assessments, while simple and cost-effective, often detect PD only after significant neurodegeneration. Neuroimaging techniques like MRI, PET, and SPECT provide insights into brain abnormalities but are expensive and require specialist interpretation. Machine learning approaches, including algorithms like SVM, DNN, and random forests, show promise in detecting subtle changes indicative of PD onset, especially when combined with wearable technologies. These methods face challenges such as overfitting, generalization, and interpretability. The paper compares the methods based on accuracy, cost, and practicality, aiming to guide future research and development in PD early detection. Here These features are analyzed using the XGBoost machine learning algorithm to classify the input and detect early signs of Parkinson's. The system provides immediate feedback via an LCD screen connected to an Arduino microcontroller, offering real-time results.