Kokkotis Christos, Moustakidis Serafeim, Baltzopoulos Vasilios, Giakas Giannis, Tsaopoulos Dimitrios
Institute for Bio-Economy & Agri-Technology, Center for Research and Technology Hellas, 60361 Volos, Greece.
Department of Physical Education & Sport Science, University of Thessaly, 38221 Trikala, Greece.
Healthcare (Basel). 2021 Mar 1;9(3):260. doi: 10.3390/healthcare9030260.
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.
膝关节骨关节炎（KOA）是一种多因素疾病，占骨关节炎疾病总负担的80%以上。KOA在进展速度方面具有异质性，有几种不同的表型和大量风险因素，这些因素常常相互作用。一些可改变和不可改变的全身及机械参数、合并症以及疼痛相关因素都促成了KOA的发展。尽管存在预测疾病发作或区分无症状患者和骨关节炎患者的模型，但近期文献中仅有少数研究关注与KOA进展相关的风险因素识别。本文通过一种强大的特征选择（FS）方法，有助于识别KOA进展的风险因素，该方法克服了两个关键挑战：（i）从骨关节炎倡议（OAI）数据库获得的现有数据具有高维度和异质性；（ii）KOA进展者类别明显小于非进展者类别所带来的严重类别不平衡问题。所提出的特征选择方法依赖于进化算法和机器学习（ML）模型的结合，从而选择出一个相对较小的由35个风险因素组成的特征子集，该子集在整个数据集上具有良好的泛化能力（平均准确率为71.25%）。我们在与知名FS技术的比较分析中，就与预测准确性和泛化能力相关的指标，研究了所提方法的有效性。使用SHapley Additive exPlanations（SHAP）进一步研究了所选风险因素对预测输出的影响。所提出的FS方法可能有助于开发新的、有效的风险分层策略，并识别每个KOA患者的风险表型，以实现适当的干预。
