Early Recognition of Parkinson's Disease Through Acoustic Analysis and Machine Learning
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 pro...
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Language | English |
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22.07.2024
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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. |
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AbstractList | 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. |
Author | Fadavi, Nazanin Fadavi, Niloofar |
Author_xml | – sequence: 1 givenname: Niloofar surname: Fadavi fullname: Fadavi, Niloofar – sequence: 2 givenname: Nazanin surname: Fadavi fullname: Fadavi, Nazanin |
BackLink | https://doi.org/10.48550/arXiv.2407.16091$$DView paper in arXiv |
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Snippet | Parkinson's Disease (PD) is a progressive neurodegenerative disorder that
significantly impacts both motor and non-motor functions, including speech.
Early and... |
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SubjectTerms | Computer Science - Learning Computer Science - Numerical Analysis Mathematics - Numerical Analysis Quantitative Biology - Neurons and Cognition |
Title | Early Recognition of Parkinson's Disease Through Acoustic Analysis and Machine Learning |
URI | https://arxiv.org/abs/2407.16091 |
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