Objective To construct and compare risk prediction models for skip metastasis in papillary thyroid carcinoma (PTC) patients with lateral lymph node metastasis (N1b) by using multiple machine learning algorithms, and to provide clinical guidance through model interpretation and visualization. MethodsA retrospective analysis of 573 N1b PTC patients who were admitted between November 2011 and August 2024 in Zhongshan Hospital Affiliated to Xiamen University and undergone primary surgery were conducted. Patients were randomly divided into training (n=402) and testing (n=171) sets according to 7∶3 ratio by using R package caret. The training set was used to build the model, and the test set was used for model validation. Five machine learning models including logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) by using 10-fold cross-validation on the training set to determine hyperparameters, then refited the models and validate them on the test set. Model performance was evaluated via area under the curve (AUC). Shapley additive explanations (SHAP) was employed for interpretability, and the optimal model was deployed as a web-based calculator using R Shiny. ResultsThe overall skip metastasis rate was 12.7% (73/573) in N1b PTC patients, with 12.9% (52/402) in the training set and 12.3% (21/171) in the testing set (P>0.05 for baseline comparisons). Eleven predictors (age, age≥55, sex, maximum tumor diameter, maximum tumor diameter≤1 cm, upper pole involvement, multifocality, unilateral lobe involvement, extrathyroidal extension, capsular invasion, and Hashimoto thyroiditis) were used to develop the model. Each model’s AUC of the training set: XGBoost, 0.824±0.070 ［95%CI (0.780, 0.868)］; LR, 0.802±0.065 ［95%CI (0.762, 0.842)］; DT, 0.773±0.141 ［95%CI (0.685, 0.861)］; RF, 0.767±0.068 ［95%CI (0.725, 0.809)］; SVM, 0.647±0.103 ［95%CI (0.583, 0.711)］. Each model’s AUC of the testing set: XGBoost, 0.777 ［95%CI (0.667, 0.887); LR, 0.769 ［95%CI (0.655, 0.883)］; DT, 0.737 ［95%CI (0.615, 0.858)］; RF, 0.757 ［95%CI (0.649, 0.865)］; SVM, 0.674 ［95%CI (0.522, 0.826)］. XGBoost was the optimum model which achieved the highest AUC in both training and testing sets. SHAP analysis identified the top six predictors: upper pole involvement (mean absolute SHAP: 0.249), maximum tumor diameter (0.119), extrathyroidal extension (0.078), age (0.065), unilateral lobe involvement (0.018), and capsular invasion (0.013). The XGBoost-based web calculator was accessible. ConclusionsThe XGBoost model demonstrates superior predictive performance among five machine learning algorithms. The developed web-based calculator offers clinical utility for assessing skip metastasis risk in N1b PTC patients.