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Vocal Feature Extraction-Based Artificial Intelligent Model for Parkinson’s Disease Detection

As a neurodegenerative disorder, Parkinson’s disease (PD) affects the nerve cells of the human brain. Early detection and treatment can help to relieve the symptoms of PD. Recent PD studies have extracted the features from vocal disorders as a harbinger for PD detection, as patients face vocal chang...

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Autores principales: Hoq, Muntasir, Uddin, Mohammed Nazim, Park, Seung-Bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8231105/
https://www.ncbi.nlm.nih.gov/pubmed/34208330
http://dx.doi.org/10.3390/diagnostics11061076
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author Hoq, Muntasir
Uddin, Mohammed Nazim
Park, Seung-Bo
author_facet Hoq, Muntasir
Uddin, Mohammed Nazim
Park, Seung-Bo
author_sort Hoq, Muntasir
collection PubMed
description As a neurodegenerative disorder, Parkinson’s disease (PD) affects the nerve cells of the human brain. Early detection and treatment can help to relieve the symptoms of PD. Recent PD studies have extracted the features from vocal disorders as a harbinger for PD detection, as patients face vocal changes and impairments at the early stages of PD. In this study, two hybrid models based on a Support Vector Machine (SVM) integrating with a Principal Component Analysis (PCA) and a Sparse Autoencoder (SAE) are proposed to detect PD patients based on their vocal features. The first model extracted and reduced the principal components of vocal features based on the explained variance of each feature using PCA. For the first time, the second model used a novel Deep Neural Network (DNN) of an SAE, consisting of multiple hidden layers with L1 regularization to compress the vocal features into lower-dimensional latent space. In both models, reduced features were fed into the SVM as inputs, which performed classification by learning hyperplanes, along with projecting the data into a higher dimension. An F1-score, a Mathews Correlation Coefficient (MCC), and a Precision-Recall curve were used, along with accuracy to evaluate the proposed models due to highly imbalanced data. With its highest accuracy of 0.935, F1-score of 0.951, and MCC value of 0.788, the probing results show that the proposed model of the SAE-SVM surpassed not only the former model of the PCA-SVM and other standard models including Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), and Random Forest (RF), but also surpassed two recent studies using the same dataset. Oversampling and balancing the dataset with SMOTE boosted the performance of the models.
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spelling pubmed-82311052021-06-26 Vocal Feature Extraction-Based Artificial Intelligent Model for Parkinson’s Disease Detection Hoq, Muntasir Uddin, Mohammed Nazim Park, Seung-Bo Diagnostics (Basel) Article As a neurodegenerative disorder, Parkinson’s disease (PD) affects the nerve cells of the human brain. Early detection and treatment can help to relieve the symptoms of PD. Recent PD studies have extracted the features from vocal disorders as a harbinger for PD detection, as patients face vocal changes and impairments at the early stages of PD. In this study, two hybrid models based on a Support Vector Machine (SVM) integrating with a Principal Component Analysis (PCA) and a Sparse Autoencoder (SAE) are proposed to detect PD patients based on their vocal features. The first model extracted and reduced the principal components of vocal features based on the explained variance of each feature using PCA. For the first time, the second model used a novel Deep Neural Network (DNN) of an SAE, consisting of multiple hidden layers with L1 regularization to compress the vocal features into lower-dimensional latent space. In both models, reduced features were fed into the SVM as inputs, which performed classification by learning hyperplanes, along with projecting the data into a higher dimension. An F1-score, a Mathews Correlation Coefficient (MCC), and a Precision-Recall curve were used, along with accuracy to evaluate the proposed models due to highly imbalanced data. With its highest accuracy of 0.935, F1-score of 0.951, and MCC value of 0.788, the probing results show that the proposed model of the SAE-SVM surpassed not only the former model of the PCA-SVM and other standard models including Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), and Random Forest (RF), but also surpassed two recent studies using the same dataset. Oversampling and balancing the dataset with SMOTE boosted the performance of the models. MDPI 2021-06-11 /pmc/articles/PMC8231105/ /pubmed/34208330 http://dx.doi.org/10.3390/diagnostics11061076 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hoq, Muntasir
Uddin, Mohammed Nazim
Park, Seung-Bo
Vocal Feature Extraction-Based Artificial Intelligent Model for Parkinson’s Disease Detection
title Vocal Feature Extraction-Based Artificial Intelligent Model for Parkinson’s Disease Detection
title_full Vocal Feature Extraction-Based Artificial Intelligent Model for Parkinson’s Disease Detection
title_fullStr Vocal Feature Extraction-Based Artificial Intelligent Model for Parkinson’s Disease Detection
title_full_unstemmed Vocal Feature Extraction-Based Artificial Intelligent Model for Parkinson’s Disease Detection
title_short Vocal Feature Extraction-Based Artificial Intelligent Model for Parkinson’s Disease Detection
title_sort vocal feature extraction-based artificial intelligent model for parkinson’s disease detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8231105/
https://www.ncbi.nlm.nih.gov/pubmed/34208330
http://dx.doi.org/10.3390/diagnostics11061076
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