Abstract
Purpose
Several methods have been developed to prevent lateral hinge fractures (LHFs), using only classic statistical models. Machine learning is under the spotlight because of its ability to analyze various weights and model nonlinear relationships. The purpose of this study was to create a machine learning model that predicts LHF with high predictive performance.
Methods
Data were collected from a total of 439 knees with medial osteoarthritis (OA) treated with Medial open wedge high tibial osteotomy (MOW-HTO) from March 2014 to February 2020. The patient data included age, sex, height, and weight. Preoperative, determined, and modifiable factors were categorized using X-ray and CT data to create ensemble models with better predictive performance. Among the 57 ensemble models, which is the total number of possible combinations with six models, the model with the highest area under curve (AUC) or F1-score was selected as the final ensemble model. Gain feature importance analysis and the Shapley additive explanations (SHAP) feature explanation were performed on the best models.
Results
The ensemble model with the highest AUC was a combination of a light gradient boosting machine (LGBM) and multilayer perceptron (MLP) (AUC = 0.992). The ensemble model with the highest F1-score was the model that combined logistic regression (LR) and MLP (F1-score = 0.765). Distance X was the most predictive feature in the results of both model interpretation analyses.
Conclusion
Two types of ensemble models, LGBM with MLP and LR with MLP, were developed as machine learning models to predict LHF with high predictive performance. Using these models, surgeons can identify important features to prevent LHF and establish strategies by adjusting modifiable factors.
Study design
Retrospective cohort study.
Level of evidence
3.
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Funding
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT). No.2021R1A2C1092657.
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HWJ, HGC and YSL participated in study design and drafted the manuscript, HWJ and MJK performed the statistical analysis, HWJ, MJK and SYP collected the data and contributed to performing statistical analysis, SYP conceived of the study, participated in coordination and helped to draft the manuscript. All authors read and approved the final.
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Jeong, H.W., Kim, M., Choi, H.G. et al. Development of a machine learning model to predict lateral hinge fractures by analyzing patient factors before open wedge high tibial osteotomy. Knee Surg Sports Traumatol Arthrosc 31, 3070–3078 (2023). https://doi.org/10.1007/s00167-022-07137-6
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DOI: https://doi.org/10.1007/s00167-022-07137-6