Improved Prediction of Knee Osteoarthritis by the Machine Learning Model XGBoost

Kui Su; Xin Yuan; Yukai Huang; Qian Yuan; Minghui Yang; Jianwu Sun; Shuyi Li; Xinyi Long; Lang Liu; Tianwang Li; Zhengqiang Yuan

doi:10.1007/s43465-023-00936-0

Improved Prediction of Knee Osteoarthritis by the Machine Learning Model XGBoost

Indian J Orthop. 2023 Jul 29;57(10):1667-1677. doi: 10.1007/s43465-023-00936-0. eCollection 2023 Oct.

Authors

Kui Su^#¹, Xin Yuan^#¹, Yukai Huang^#², Qian Yuan¹, Minghui Yang¹, Jianwu Sun¹, Shuyi Li¹, Xinyi Long¹, Lang Liu¹, Tianwang Li², Zhengqiang Yuan¹

Affiliations

¹ School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Higher Education Mega Center, 100 Outside Ring West Road, Guangzhou, 510006 People's Republic of China.
² Department of Rheumatology and Immunology, Guangdong Second Provincial General Hospital, Guangzhou, 510317 People's Republic of China.

^# Contributed equally.

PMID: 37766962
PMCID: PMC10519887 (available on 2024-07-29)
DOI: 10.1007/s43465-023-00936-0

Abstract

Objectives: The accurate prediction of osteoarthritis (OA) severity in patients can be helpful to make the proper decision of intervention. This study aims to build up a powerful model to assess predictive risk factors and severity of knee osteoarthritis (KOA) in the clinical scenario.

Methods: A total of 4796 KOA cases and 1205 features were selected by feature selections from the public OA database, Osteoarthritis Initiative (OAI). Six machine learning-based models were constructed and compared for the accuracy of OA prediction. The gradient-boosting decision tree was used to identify important prediction features in the extreme gradient boosting (XGBoost) model. The performance of models was evaluated by F1-score.

Results: Twenty features were determined as predictors for KOA risk and severity, including the subject characteristics, knee symptoms/risk factors and physical exam. The XGBoost model demonstrated 100% prediction accuracy for 54.7% of examined samples, and the remaining 45.3% of samples showed Kellgren and Lawrence (KL) gradings very close to the actual levels. It showed the highest prediction accuracy with an F1-score of 0.553 among the tested six models.

Conclusions: We demonstrate that the XGBoost is the best model for the prediction of KOA severity in the six examined models. In addition, 20 risk features were determined as the essential predictors of KOA, including the physical exam, knee symptoms/risk factors and subject characteristics, which may be useful for the identification of high-risk KOA cases and for making appropriate treatment decisions as well.

Keywords: KOA prediction; Machine learning; Risk factor; Severity assessment; XGBoost.

© Indian Orthopaedics Association 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.