XGBoosting Early Detection: Advancing Parkinson’s Disease Diagnosis through Machine Learning

Abstract

Parkinson's disease (PD) is a significant neurodegenerative disorder, affecting millions worldwide. Early and accurate diagnosis is pivotal for effective treatment. In this study, we explore the application of XGBoost, a powerful machine learning algorithm, for classifying PD based on speech signal features. Our research presents a systematic methodology, addressing data preparation, model development, and performance evaluation. The XGBoost model achieved an accuracy of 80.09% in distinguishing individuals with and without PD, demonstrating its promise for disease classification. Moreover, we discuss the potential for XGBoost in healthcare and highlight the need for further research in the intersection of machine learning and disease diagnosis.

Advancing Skin Cancer Detection: Harnessing the Power of CNNs

“…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.…”