Table 1_Prediction of discoid lateral meniscus based on tibial plateau anatomical factors and machine learning.xlsx

Subsequently, the Least Absolute Shrinkage with Selection Operator (LASSO) regression algorithm was used to screen the feature variables. Seven machine learning prediction models (including: Random Forest, XGBoost, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbor (KNN), Naive Bayes, and Gradient Boosting) were constructed based on the screened feature variables, and the best model was selected. Using SHAP (Shapley Additive Explanations) to quantitatively analyze the contribution of each characteristic variable, thereby assessing the relative importance of the characteristic factors and increasing the interpretability of the model.</p>Results<p>Gender, lateral joint space, height of lateral tibial spine, lateral slope of the lateral tibial spine, lateral slope of the medial tibial spine, and tibial eminence width/tibial plateau width can be used as characteristic parameters of lateral discoid meniscus and lateral incomplete discoid meniscus.

Based on these parameters, the best-performing models achieved good discriminative performance for both CDLM and ICDLM in the held-out validation sets, with AUC values of 0.885 and 0.861, respectively.</p>Conclusion<p>Tibial plateau anatomical parameters may facilitate the identification of DLM. Machine-learning models integrating these parameters provide an efficient radiograph-based approach to support the early recognition of DLM.</p>