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Kokkotis, C, Moustakidis, S, Baltzopoulos, V, Giakas, G and Tsaopoulos, D (2021) Identifying Robust Risk Factors for Knee Osteoarthritis Progression: An Evolutionary Machine Learning Approach. Healthcare, 9 (3). ISSN 2227-9032
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Identifying Robust Risk Factors for Knee Osteoarthritis Progression An Evolutionary Machine Learning Approach.pdf - Published Version Available under License Creative Commons Attribution. Download (17MB) | Preview |
Abstract
Knee osteoarthritis (KOA) is a multifactorial disease which is responsible for more than 80% of the osteoarthritis disease’s total burden. KOA is heterogeneous in terms of rates of progression with several different phenotypes and a large number of risk factors, which often interact with each other. A number of modifiable and non-modifiable systemic and mechanical parameters along with comorbidities as well as pain-related factors contribute to the development of KOA. Although models exist to predict the onset of the disease or discriminate between asymptotic and OA patients, there are just a few studies in the recent literature that focused on the identification of risk factors associated with KOA progression. This paper contributes to the identification of risk factors for KOA progression via a robust feature selection (FS) methodology that overcomes two crucial challenges: (i) the observed high dimensionality and heterogeneity of the available data that are obtained from the Osteoarthritis Initiative (OAI) database and (ii) a severe class imbalance problem posed by the fact that the KOA progressors class is significantly smaller than the non-progressors’ class. The proposed feature selection methodology relies on a combination of evolutionary algorithms and machine learning (ML) models, leading to the selection of a relatively small feature subset of 35 risk factors that generalizes well on the whole dataset (mean accuracy of 71.25%). We investigated the effectiveness of the proposed approach in a comparative analysis with well-known FS techniques with respect to metrics related to both prediction accuracy and generalization capability. The impact of the selected risk factors on the prediction output was further investigated using SHapley Additive exPlanations (SHAP). The proposed FS methodology may contribute to the development of new, efficient risk stratification strategies and identification of risk phenotypes of each KOA patient to enable appropriate interventions.
| Item Type: | Article |
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| Uncontrolled Keywords: | Science & Technology; Life Sciences & Biomedicine; Health Care Sciences & Services; Health Policy & Services; knee osteoarthritis prediction; feature selection; genetic algorithm; machine learning; explainability; SUPPORT VECTOR MACHINES; PREDICTION; IDENTIFICATION; FEATURES; SMOTE; explainability; feature selection; genetic algorithm; knee osteoarthritis prediction; machine learning |
| Subjects: | R Medicine > RC Internal medicine > RC1200 Sports Medicine |
| Divisions: | Sport & Exercise Sciences |
| Publisher: | MDPI |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 19 May 2022 09:53 |
| Last Modified: | 19 May 2022 10:00 |
| DOI or ID number: | 10.3390/healthcare9030260 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/16871 |
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