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Comparative analysis of machine learning in diagnosing Parkinson's: Utilizing vocal characteristics
- 1 Hillsborough High School
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Abstract
Parkinson’s disease is a neurodegenerative disorder that affects movement. Diagnosing Parkinson’s disease has traditionally involved clinical assessments by neurologists, and this practice still persists today to a significant extent. However, clinical assessments can be prone to subjectivity. In this study, a comprehensive predictive modeling approach was undertaken, employing nine dis¬tinct machine learning algorithms and six different model evaluation metrics to identify the best per¬forming algorithms. The findings reveal that, using only 12 vocal characteristics, KNeighborsClassfier (KNC), MLPClassifier (MLP), and XGBClassifier (XGBC) achieved the highest score of 0.87. This score is generally considered very good, indicating that the model is robust and possesses strong predictive power. This study marks a crucial initial step in leveraging machine learning techniques for more effective and potentially more accurate diagnosis of Parkinson’s disease based on patients’ vocal characteristics.
Keywords
Parkinson’s disease, machine learning, vocal characteristics.
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Cite this article
Yu,Y. (2024). Comparative analysis of machine learning in diagnosing Parkinson's: Utilizing vocal characteristics . Theoretical and Natural Science,40,1-8.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 4th International Conference on Biological Engineering and Medical Science
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