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Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears
In recent times, knee joint pains have become severe enough to make daily tasks difficult. Knee osteoarthritis is a type of arthritis and a leading cause of disability worldwide. The middle of the knee contains a vital portion, the anterior cruciate ligament (ACL). It is necessary to diagnose the AC...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9015864/ https://www.ncbi.nlm.nih.gov/pubmed/35444781 http://dx.doi.org/10.1155/2022/2550120 |
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author | Awan, Mazhar Javed Mohd Rahim, Mohd Shafry Salim, Naomie Rehman, Amjad Nobanee, Haitham |
author_facet | Awan, Mazhar Javed Mohd Rahim, Mohd Shafry Salim, Naomie Rehman, Amjad Nobanee, Haitham |
author_sort | Awan, Mazhar Javed |
collection | PubMed |
description | In recent times, knee joint pains have become severe enough to make daily tasks difficult. Knee osteoarthritis is a type of arthritis and a leading cause of disability worldwide. The middle of the knee contains a vital portion, the anterior cruciate ligament (ACL). It is necessary to diagnose the ACL ruptured tears early to avoid surgery. The study aimed to perform a comparative analysis of machine learning models to identify the condition of three ACL tears. In contrast to previous studies, this study also considers imbalanced data distributions as machine learning techniques struggle to deal with this problem. The paper applied and analyzed four machine learning classification models, namely, random forest (RF), categorical boosting (Cat Boost), light gradient boosting machines (LGBM), and highly randomized classifier (ETC) on the balanced, structured dataset of ACL. After oversampling a hyperparameter adjustment, the above four models have achieved an average accuracy of 95.72%, 94.98%, 94.98%, and 98.26%. There are 2070 observations and eight features in the collection of three diagnosis ACL classes after oversampling. The area under curve value was approximately 0.998, respectively. Experiments were performed using twelve machine learning algorithms with imbalanced and balanced datasets. However, the accuracy of the imbalanced dataset has remained under 76% for all twelve models. After oversampling, the proposed model may contribute to the investigation of ACL tears on magnetic resonance imaging and other knee ligaments efficiently and automatically without involving radiologists. |
format | Online Article Text |
id | pubmed-9015864 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-90158642022-04-19 Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears Awan, Mazhar Javed Mohd Rahim, Mohd Shafry Salim, Naomie Rehman, Amjad Nobanee, Haitham J Healthc Eng Research Article In recent times, knee joint pains have become severe enough to make daily tasks difficult. Knee osteoarthritis is a type of arthritis and a leading cause of disability worldwide. The middle of the knee contains a vital portion, the anterior cruciate ligament (ACL). It is necessary to diagnose the ACL ruptured tears early to avoid surgery. The study aimed to perform a comparative analysis of machine learning models to identify the condition of three ACL tears. In contrast to previous studies, this study also considers imbalanced data distributions as machine learning techniques struggle to deal with this problem. The paper applied and analyzed four machine learning classification models, namely, random forest (RF), categorical boosting (Cat Boost), light gradient boosting machines (LGBM), and highly randomized classifier (ETC) on the balanced, structured dataset of ACL. After oversampling a hyperparameter adjustment, the above four models have achieved an average accuracy of 95.72%, 94.98%, 94.98%, and 98.26%. There are 2070 observations and eight features in the collection of three diagnosis ACL classes after oversampling. The area under curve value was approximately 0.998, respectively. Experiments were performed using twelve machine learning algorithms with imbalanced and balanced datasets. However, the accuracy of the imbalanced dataset has remained under 76% for all twelve models. After oversampling, the proposed model may contribute to the investigation of ACL tears on magnetic resonance imaging and other knee ligaments efficiently and automatically without involving radiologists. Hindawi 2022-04-11 /pmc/articles/PMC9015864/ /pubmed/35444781 http://dx.doi.org/10.1155/2022/2550120 Text en Copyright © 2022 Mazhar Javed Awan et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Awan, Mazhar Javed Mohd Rahim, Mohd Shafry Salim, Naomie Rehman, Amjad Nobanee, Haitham Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears |
title | Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears |
title_full | Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears |
title_fullStr | Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears |
title_full_unstemmed | Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears |
title_short | Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears |
title_sort | machine learning-based performance comparison to diagnose anterior cruciate ligament tears |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9015864/ https://www.ncbi.nlm.nih.gov/pubmed/35444781 http://dx.doi.org/10.1155/2022/2550120 |
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