Machine Learning Based Early Detection of Parkinson’s Disease using XGBoost and Random Forest
DOI:
https://doi.org/10.48047/exqhwd77Keywords:
Parkinson’s Disease (PD), Machine Learning (ML), Voice Pattern Analysis, XGBoost, Random Forest, Multimodel data, early detection.Abstract
This study synthesizes current research on machine learning (ML) and deep learning (DL) methodologies for the detection of Parkinson’s Disease (PD). Leveraging datasets such as the UCI Parkinson’s and Oxford Parkinson’s datasets, various algorithms—including XGBoost, Random Forest, Support Vector Machines (SVM), and Feedforward Neural Networks—have demonstrated impressive diagnostic accuracy, achieving rates as high as 99.11% by analyzing key voice features like jitter, shimmer, and harmonic-to-noise ratio (HNR). Additionally, multimodal approaches that integrate voice data with clinical and imaging information have further improved diagnostic precision.
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References
M. Driendl and P. J. Gonçalves, "Feature Selection for Parkinson’s Disease Prediction: An Empirical Study," Journal of Biomedical Informatics, vol. 119, p. 103898, 2022. [2] A. Singh, R. Gupta, and V. Sharma, "Parkinson’s Disease Detection Using Machine Learning and Voice Biomarkers," International Journal of Medical Informatics, vol. 147, p. 104339, 2021.
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