Awan, Mazhar Javed, Rahim, Mohd Shafry Mohd, Salim, Naomie, Rehman, Amjad and Nobanee, Haitham
(2022)
Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears.
JOURNAL OF HEALTHCARE ENGINEERING, 2022.
2550120-.
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Abstract
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.
Item Type: | Article |
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Uncontrolled Keywords: | Knee, Anterior Cruciate Ligament, Knee Joint, Humans, Magnetic Resonance Imaging, Machine Learning, Anterior Cruciate Ligament Injuries |
Divisions: | Faculty of Humanities and Social Sciences > School of Histories, Languages and Cultures |
Depositing User: | Symplectic Admin |
Date Deposited: | 22 Jul 2022 15:28 |
Last Modified: | 18 Jan 2023 20:55 |
DOI: | 10.1155/2022/2550120 |
Open Access URL: | https://www.hindawi.com/journals/jhe/2022/2550120/ |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/3159204 |