Cargando…
Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms
Medical diagnosis through the classification of biomedical attributes is one of the exponentially growing fields in bioinformatics. Although a large number of approaches have been presented in the past, wide use and superior performance of the machine learning (ML) methods in medical diagnosis neces...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323723/ https://www.ncbi.nlm.nih.gov/pubmed/34395856 http://dx.doi.org/10.7717/peerj-cs.547 |
_version_ | 1783731298942058496 |
---|---|
author | Reshi, Aijaz Ahmad Ashraf, Imran Rustam, Furqan Shahzad, Hina Fatima Mehmood, Arif Choi, Gyu Sang |
author_facet | Reshi, Aijaz Ahmad Ashraf, Imran Rustam, Furqan Shahzad, Hina Fatima Mehmood, Arif Choi, Gyu Sang |
author_sort | Reshi, Aijaz Ahmad |
collection | PubMed |
description | Medical diagnosis through the classification of biomedical attributes is one of the exponentially growing fields in bioinformatics. Although a large number of approaches have been presented in the past, wide use and superior performance of the machine learning (ML) methods in medical diagnosis necessitates significant consideration for automatic diagnostic methods. This study proposes a novel approach called concatenated resampling (CR) to increase the efficacy of traditional ML algorithms. The performance is analyzed leveraging four ML approaches like tree-based ensemble approaches, and linear machine learning approach for automatic diagnosis of inter-vertebral pathologies with increased. Besides, undersampling, over-sampling, and proposed CR techniques have been applied to unbalanced training dataset to analyze the impact of these techniques on the accuracy of each of the classification model. Extensive experiments have been conducted to make comparisons among different classification models using several metrics including accuracy, precision, recall, and F(1) score. Comparative analysis has been performed on the experimental results to identify the best performing classifier along with the application of the re-sampling technique. The results show that the extra tree classifier achieves an accuracy of 0.99 in association with the proposed CR technique. |
format | Online Article Text |
id | pubmed-8323723 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-83237232021-08-13 Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms Reshi, Aijaz Ahmad Ashraf, Imran Rustam, Furqan Shahzad, Hina Fatima Mehmood, Arif Choi, Gyu Sang PeerJ Comput Sci Bioinformatics Medical diagnosis through the classification of biomedical attributes is one of the exponentially growing fields in bioinformatics. Although a large number of approaches have been presented in the past, wide use and superior performance of the machine learning (ML) methods in medical diagnosis necessitates significant consideration for automatic diagnostic methods. This study proposes a novel approach called concatenated resampling (CR) to increase the efficacy of traditional ML algorithms. The performance is analyzed leveraging four ML approaches like tree-based ensemble approaches, and linear machine learning approach for automatic diagnosis of inter-vertebral pathologies with increased. Besides, undersampling, over-sampling, and proposed CR techniques have been applied to unbalanced training dataset to analyze the impact of these techniques on the accuracy of each of the classification model. Extensive experiments have been conducted to make comparisons among different classification models using several metrics including accuracy, precision, recall, and F(1) score. Comparative analysis has been performed on the experimental results to identify the best performing classifier along with the application of the re-sampling technique. The results show that the extra tree classifier achieves an accuracy of 0.99 in association with the proposed CR technique. PeerJ Inc. 2021-07-22 /pmc/articles/PMC8323723/ /pubmed/34395856 http://dx.doi.org/10.7717/peerj-cs.547 Text en © 2021 Reshi et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Reshi, Aijaz Ahmad Ashraf, Imran Rustam, Furqan Shahzad, Hina Fatima Mehmood, Arif Choi, Gyu Sang Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms |
title | Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms |
title_full | Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms |
title_fullStr | Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms |
title_full_unstemmed | Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms |
title_short | Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms |
title_sort | diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323723/ https://www.ncbi.nlm.nih.gov/pubmed/34395856 http://dx.doi.org/10.7717/peerj-cs.547 |
work_keys_str_mv | AT reshiaijazahmad diagnosisofvertebralcolumnpathologiesusingconcatenatedresamplingwithmachinelearningalgorithms AT ashrafimran diagnosisofvertebralcolumnpathologiesusingconcatenatedresamplingwithmachinelearningalgorithms AT rustamfurqan diagnosisofvertebralcolumnpathologiesusingconcatenatedresamplingwithmachinelearningalgorithms AT shahzadhinafatima diagnosisofvertebralcolumnpathologiesusingconcatenatedresamplingwithmachinelearningalgorithms AT mehmoodarif diagnosisofvertebralcolumnpathologiesusingconcatenatedresamplingwithmachinelearningalgorithms AT choigyusang diagnosisofvertebralcolumnpathologiesusingconcatenatedresamplingwithmachinelearningalgorithms |