Keywords
Parkinson’s disease, Logistic Regression, Gaussian Naive Bayes, K Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost), Support Vector Machines
Abstract
The early detection of diseases profoundly influences treatment efficacy, and accurate classification methodologies are essential for effective disease identification. In this project, we examined fve different classifers—Logistic Regression, Gaussian Naive Bayes, K Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost), and Support Vector Machines—and evaluated their performance in detecting Parkinson’s disease (PD) based on voice features. The study aims to identify the best classifier for detecting PD. XGBoost performed the best, with an accuracy of 91% on the full dataset. After variable selection, KNN had the best performance with an accuracy of 91%. These findings suggest that Machine learning algorithms(classifiers) can offer valuable insights into disease detection.
Course Name
STA 6366 Data Science 1
Instructor Name
Dr Rui Xie
STARS Citation
Yeboah, Felix, "Uncovering Acoustic Biomarkers to Classify Parkinson Disease through Machine Learning" (2025). Data Science and Data Mining. 34.
https://stars.library.ucf.edu/data-science-mining/34
Included in
Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons, Data Science Commons
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