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A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson’s Disease Detection
Parkinson’s disease (PD), which is a slowly progressing neurodegenerative disorder, negatively affects people’s daily lives. Early diagnosis is of great importance to minimize the effects of PD. One of the most important symptoms in the early diagnosis of PD disease is the monotony and distortion of...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8781034/ https://www.ncbi.nlm.nih.gov/pubmed/35055370 http://dx.doi.org/10.3390/jpm12010055 |
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author | Demir, Fatih Siddique, Kamran Alswaitti, Mohammed Demir, Kursat Sengur, Abdulkadir |
author_facet | Demir, Fatih Siddique, Kamran Alswaitti, Mohammed Demir, Kursat Sengur, Abdulkadir |
author_sort | Demir, Fatih |
collection | PubMed |
description | Parkinson’s disease (PD), which is a slowly progressing neurodegenerative disorder, negatively affects people’s daily lives. Early diagnosis is of great importance to minimize the effects of PD. One of the most important symptoms in the early diagnosis of PD disease is the monotony and distortion of speech. Artificial intelligence-based approaches can help specialists and physicians to automatically detect these disorders. In this study, a new and powerful approach based on multi-level feature selection was proposed to detect PD from features containing voice recordings of already-diagnosed cases. At the first level, feature selection was performed with the Chi-square and L1-Norm SVM algorithms (CLS). Then, the features that were extracted from these algorithms were combined to increase the representation power of the samples. At the last level, those samples that were highly distinctive from the combined feature set were selected with feature importance weights using the ReliefF algorithm. In the classification stage, popular classifiers such as KNN, SVM, and DT were used for machine learning, and the best performance was achieved with the KNN classifier. Moreover, the hyperparameters of the KNN classifier were selected with the Bayesian optimization algorithm, and the performance of the proposed approach was further improved. The proposed approach was evaluated using a 10-fold cross-validation technique on a dataset containing PD and normal classes, and a classification accuracy of 95.4% was achieved. |
format | Online Article Text |
id | pubmed-8781034 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87810342022-01-22 A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson’s Disease Detection Demir, Fatih Siddique, Kamran Alswaitti, Mohammed Demir, Kursat Sengur, Abdulkadir J Pers Med Article Parkinson’s disease (PD), which is a slowly progressing neurodegenerative disorder, negatively affects people’s daily lives. Early diagnosis is of great importance to minimize the effects of PD. One of the most important symptoms in the early diagnosis of PD disease is the monotony and distortion of speech. Artificial intelligence-based approaches can help specialists and physicians to automatically detect these disorders. In this study, a new and powerful approach based on multi-level feature selection was proposed to detect PD from features containing voice recordings of already-diagnosed cases. At the first level, feature selection was performed with the Chi-square and L1-Norm SVM algorithms (CLS). Then, the features that were extracted from these algorithms were combined to increase the representation power of the samples. At the last level, those samples that were highly distinctive from the combined feature set were selected with feature importance weights using the ReliefF algorithm. In the classification stage, popular classifiers such as KNN, SVM, and DT were used for machine learning, and the best performance was achieved with the KNN classifier. Moreover, the hyperparameters of the KNN classifier were selected with the Bayesian optimization algorithm, and the performance of the proposed approach was further improved. The proposed approach was evaluated using a 10-fold cross-validation technique on a dataset containing PD and normal classes, and a classification accuracy of 95.4% was achieved. MDPI 2022-01-06 /pmc/articles/PMC8781034/ /pubmed/35055370 http://dx.doi.org/10.3390/jpm12010055 Text en © 2022 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 Demir, Fatih Siddique, Kamran Alswaitti, Mohammed Demir, Kursat Sengur, Abdulkadir A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson’s Disease Detection |
title | A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson’s Disease Detection |
title_full | A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson’s Disease Detection |
title_fullStr | A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson’s Disease Detection |
title_full_unstemmed | A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson’s Disease Detection |
title_short | A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson’s Disease Detection |
title_sort | simple and effective approach based on a multi-level feature selection for automated parkinson’s disease detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8781034/ https://www.ncbi.nlm.nih.gov/pubmed/35055370 http://dx.doi.org/10.3390/jpm12010055 |
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