Parkinson’s Disease Detection using XGBoost and Machine Learning

This article explores the application of machine learning, specifically the XGBoost algorithm, for the early detection of Parkinson’s disease. Parkinson’s disease is a prevalent neurodegenerative condition that poses diagnostic challenges, particularly in its early stages. To address these challenges, a comprehensive dataset, including vocal frequency measurements, audio analyses, and demographic data, is employed. Data preprocessing techniques, including Min-Max scaling and Synthetic Minority Over-sampling Technique (SMOTE), are applied to prepare the dataset. The XGBoost model is then developed and fine-tuned to achieve an accuracy of approximately 93.33% in detecting Parkinson’s disease. The model exhibits high precision, recall, and F1-Score, making it a valuable tool for early disease detection in the healthcare domain. The study highlights the transformative potential of machine learning in improving patient outcomes and healthcare efficiency.