Cargando…
Analysis of Parkinson’s Disease Using an Imbalanced-Speech Dataset by Employing Decision Tree Ensemble Methods
Parkinson’s disease (PD) currently affects approximately 10 million people worldwide. The detection of PD positive subjects is vital in terms of disease prognostics, diagnostics, management and treatment. Different types of early symptoms, such as speech impairment and changes in writing, are associ...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9776735/ https://www.ncbi.nlm.nih.gov/pubmed/36553007 http://dx.doi.org/10.3390/diagnostics12123000 |
_version_ | 1784855934559846400 |
---|---|
author | Barukab, Omar Ahmad, Amir Khan, Tabrej Thayyil Kunhumuhammed, Mujeeb Rahiman |
author_facet | Barukab, Omar Ahmad, Amir Khan, Tabrej Thayyil Kunhumuhammed, Mujeeb Rahiman |
author_sort | Barukab, Omar |
collection | PubMed |
description | Parkinson’s disease (PD) currently affects approximately 10 million people worldwide. The detection of PD positive subjects is vital in terms of disease prognostics, diagnostics, management and treatment. Different types of early symptoms, such as speech impairment and changes in writing, are associated with Parkinson disease. To classify potential patients of PD, many researchers used machine learning algorithms in various datasets related to this disease. In our research, we study the dataset of the PD vocal impairment feature, which is an imbalanced dataset. We propose comparative performance evaluation using various decision tree ensemble methods, with or without oversampling techniques. In addition, we compare the performance of classifiers with different sizes of ensembles and various ratios of the minority class and the majority class with oversampling and undersampling. Finally, we combine feature selection with best-performing ensemble classifiers. The result shows that AdaBoost, random forest, and decision tree developed for the RUSBoost imbalanced dataset perform well in performance metrics such as precision, recall, F1-score, area under the receiver operating characteristic curve (AUROC) and the geometric mean. Further, feature selection methods, namely lasso and information gain, were used to screen the 10 best features using the best ensemble classifiers. AdaBoost with information gain feature selection method is the best performing ensemble method with an F1-score of 0.903. |
format | Online Article Text |
id | pubmed-9776735 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97767352022-12-23 Analysis of Parkinson’s Disease Using an Imbalanced-Speech Dataset by Employing Decision Tree Ensemble Methods Barukab, Omar Ahmad, Amir Khan, Tabrej Thayyil Kunhumuhammed, Mujeeb Rahiman Diagnostics (Basel) Article Parkinson’s disease (PD) currently affects approximately 10 million people worldwide. The detection of PD positive subjects is vital in terms of disease prognostics, diagnostics, management and treatment. Different types of early symptoms, such as speech impairment and changes in writing, are associated with Parkinson disease. To classify potential patients of PD, many researchers used machine learning algorithms in various datasets related to this disease. In our research, we study the dataset of the PD vocal impairment feature, which is an imbalanced dataset. We propose comparative performance evaluation using various decision tree ensemble methods, with or without oversampling techniques. In addition, we compare the performance of classifiers with different sizes of ensembles and various ratios of the minority class and the majority class with oversampling and undersampling. Finally, we combine feature selection with best-performing ensemble classifiers. The result shows that AdaBoost, random forest, and decision tree developed for the RUSBoost imbalanced dataset perform well in performance metrics such as precision, recall, F1-score, area under the receiver operating characteristic curve (AUROC) and the geometric mean. Further, feature selection methods, namely lasso and information gain, were used to screen the 10 best features using the best ensemble classifiers. AdaBoost with information gain feature selection method is the best performing ensemble method with an F1-score of 0.903. MDPI 2022-11-30 /pmc/articles/PMC9776735/ /pubmed/36553007 http://dx.doi.org/10.3390/diagnostics12123000 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 Barukab, Omar Ahmad, Amir Khan, Tabrej Thayyil Kunhumuhammed, Mujeeb Rahiman Analysis of Parkinson’s Disease Using an Imbalanced-Speech Dataset by Employing Decision Tree Ensemble Methods |
title | Analysis of Parkinson’s Disease Using an Imbalanced-Speech Dataset by Employing Decision Tree Ensemble Methods |
title_full | Analysis of Parkinson’s Disease Using an Imbalanced-Speech Dataset by Employing Decision Tree Ensemble Methods |
title_fullStr | Analysis of Parkinson’s Disease Using an Imbalanced-Speech Dataset by Employing Decision Tree Ensemble Methods |
title_full_unstemmed | Analysis of Parkinson’s Disease Using an Imbalanced-Speech Dataset by Employing Decision Tree Ensemble Methods |
title_short | Analysis of Parkinson’s Disease Using an Imbalanced-Speech Dataset by Employing Decision Tree Ensemble Methods |
title_sort | analysis of parkinson’s disease using an imbalanced-speech dataset by employing decision tree ensemble methods |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9776735/ https://www.ncbi.nlm.nih.gov/pubmed/36553007 http://dx.doi.org/10.3390/diagnostics12123000 |
work_keys_str_mv | AT barukabomar analysisofparkinsonsdiseaseusinganimbalancedspeechdatasetbyemployingdecisiontreeensemblemethods AT ahmadamir analysisofparkinsonsdiseaseusinganimbalancedspeechdatasetbyemployingdecisiontreeensemblemethods AT khantabrej analysisofparkinsonsdiseaseusinganimbalancedspeechdatasetbyemployingdecisiontreeensemblemethods AT thayyilkunhumuhammedmujeebrahiman analysisofparkinsonsdiseaseusinganimbalancedspeechdatasetbyemployingdecisiontreeensemblemethods |