Parkinson’s Disease Detection

Abstract: Parkinson’s disease (PD) is a progressive neurological disorder that affects movement control. It is often marked by tremors, stiffness, and slowed motion. Early and precise detection of Parkinson’s disease is vital for effective treatment and management, as it can greatly enhance patients’ quality of life. Recently, machine learning and signal processing techniques have proven promising in identifying Parkinson’s disease using various biomedical signals, including voice recordings, handwriting patterns, and gait analysis. By extracting key features and training classification models, these systems can differentiate between healthy individuals and those with Parkinson’s disease with high accuracy. This study aims to create a reliable detection model that uses data-driven approaches to support medical diagnosis and enable early intervention. The proposed method seeks to improve diagnostic efficiency, minimize human error, and contribute to better healthcare systems.

Keywords: Parkinson’s Disease (PD), Early Detection, Neurological Disorder, Machine Learning, Data Preprocessing, Accuracy and Performance Evaluation.