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Article

Identifying Robust Risk Factors for Knee Osteoarthritis Progression: An Evolutionary Machine Learning Approach

1
Institute for Bio-Economy & Agri-Technology, Center for Research and Technology Hellas, 60361 Volos, Greece
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Department of Physical Education & Sport Science, University of Thessaly, 38221 Trikala, Greece
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AIDEAS OÜ, Narva mnt 5, Tallinn, 10117 Harju maakond, Estonia
4
Research Institute for Sport and Exercises Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Tin-Chih Chen
Healthcare 2021, 9(3), 260; https://doi.org/10.3390/healthcare9030260
Received: 3 January 2021 / Revised: 22 February 2021 / Accepted: 23 February 2021 / Published: 1 March 2021
(This article belongs to the Section Artificial Intelligence in Medicine)
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. View Full-Text
Keywords: knee osteoarthritis prediction; feature selection; genetic algorithm; machine learning; explainability knee osteoarthritis prediction; feature selection; genetic algorithm; machine learning; explainability
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MDPI and ACS Style

Kokkotis, C.; Moustakidis, S.; Baltzopoulos, V.; Giakas, G.; Tsaopoulos, D. Identifying Robust Risk Factors for Knee Osteoarthritis Progression: An Evolutionary Machine Learning Approach. Healthcare 2021, 9, 260. https://doi.org/10.3390/healthcare9030260

AMA Style

Kokkotis C, Moustakidis S, Baltzopoulos V, Giakas G, Tsaopoulos D. Identifying Robust Risk Factors for Knee Osteoarthritis Progression: An Evolutionary Machine Learning Approach. Healthcare. 2021; 9(3):260. https://doi.org/10.3390/healthcare9030260

Chicago/Turabian Style

Kokkotis, Christos, Serafeim Moustakidis, Vasilios Baltzopoulos, Giannis Giakas, and Dimitrios Tsaopoulos. 2021. "Identifying Robust Risk Factors for Knee Osteoarthritis Progression: An Evolutionary Machine Learning Approach" Healthcare 9, no. 3: 260. https://doi.org/10.3390/healthcare9030260

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