Parkinson’s Disease Detection using XGBoost and Machine Learning

Abstract

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.

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“…It is noteworthy that while these visualization techniques offer valuable insights, they should be interpreted in conjunction with other evaluation metrics, as noted by Saito et al, to ensure a comprehensive assessment of model performance and to guide further improvements [8]. Overall, the utilization of these visualization techniques, alongside rigorous evaluation methodologies [9][10][11][12][13][14][15][16][17], contributes to advancing the field of classification model assessment and refinement.…”