Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Parkinson’s
Disease (PD) is a chronic neurological condition that significantly affects
speech and motor control. Early diagnosis plays a vital role in symptom
management and slowing disease progression. This project presents an automated
machine learning-based system for early detection and severity classification
of Parkinson’s Disease using voice signal features. Key voice measurements such
as jitter, shimmer, and harmonic-to-noise ratio are extracted from biomedical
voice data to train multiple classifiers. An ensemble model combining XGBoost,
K-Nearest Neighbors, Decision Tree, and Gaussian Naive Bayes achieves high
diagnostic accuracy. The system also incorporates severity prediction (Mild,
Moderate, Severe) based on probability scores and provides personalized
recommendations related to exercise, diet, and therapy. The best-performing
model is deployed in a Flask-based web application, enabling users to input
voice features and receive real-time feedback. This non-invasive, cost-
effective, and user-friendly system aids in clinical diagnosis, enhances early
detection, and empowers patients with actionable health insights.
Country : India
IRJIET, Volume 9, Special Issue of ICCIS-2025 May 2025 pp. 150-154