Early recognition of parkinson's disease through acoustic analysis and machine learning
1 Department of Operations Research and Engineering management, Southern Methodist University, Dallas, TX, USA.
2 Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Tehran, Iran.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(02), 1376–1388
Publication history:
Received on 16 June 2024; revised on 10 August 2024; accepted on 13 August 2024
Abstract:
Parkinson's Disease (PD) is a progressive neurodegenerative disorder that significantly impacts both motor and non-motor functions, including speech. Early and accurate recognition of PD through speech analysis can greatly enhance patient outcomes by enabling timely intervention. This paper provides a comprehensive review of methods for PD recognition using speech data, highlighting advances in machine learning and data-driven approaches. We discuss the process of data wrangling, including data collection, cleaning, transformation, and exploratory data analysis, to prepare the dataset for machine learning applications. Various classification algorithms are explored, including logistic regression, SVM, and neural networks, with and without feature selection. Each method is evaluated based on accuracy, precision, and training time. Our findings indicate that specific acoustic features and advanced machine-learning techniques can effectively differentiate between individuals with PD and healthy controls. The study concludes with a comparison of the different models, identifying the most effective approaches for PD recognition, and suggesting potential directions for future research.
Keywords:
Parkinson; SVM; Neural networks; Logistic regression
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0