To develop and compare multiple risk prediction models for postoperative hypocalcemia in patients with thyroid cancer, identify core predictive factors, and provide a scientific tool for early clinical warning and precision intervention to optimize perioperative management.

A total of 767 patients who underwent surgery for differentiated thyroid cancer were retrospectively enrolled from April 1, 2022 to August 31, 2025 in Huizhou First People’s Hospital, with the occurrence of postoperative hypocalcemia as the primary outcome. Core predictors were selected using least absolute shrinkage and selection operator (LASSO) regression. Based on these predictors, three prediction models-a multivariate nomogram, a decision tree, and an extreme gradient boosting (XGBoost) model-were constructed. The dataset was split into training and testing sets at an 8∶2 ratio using stratified sampling. Model performance was evaluated using accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve (AUC). SHapley additive explanations (SHAP) values were employed to interpret feature contributions in the XGBoost model.

Among the 767 patients, 233 (30.4%) developed postoperative hypocalcemia. LASSO regression identified nine core predictors: age, body mass index (BMI), postoperative parathyroid hormone (PTH) level, extent of surgery, recurrence risk, presence of papillary microcarcinoma, number of tumors, history of diabetes mellitus, and thyroiditis. All three models demonstrated good discriminative ability (AUC≥0.828). The XGBoost model achieved the highest AUC (0.851) but the lowest recall (0.435). The decision tree model showed the best recall (0.587) and F1 score (0.651). The nomogram model yielded the highest accuracy (0.816) and precision (0.781), and enabled convenient, visualized individualized risk assessment. SHAP analysis confirmed that postoperative PTH level and extent of surgery were the most influential predictors.

The nine core predictors identified in this study hold significant value for predicting postoperative hypocalcemia in thyroid cancer patients. Among the models evaluated, the nomogram offers the best balance of accuracy, usability, and clinical interpretability, making it the optimal tool for clinical application. It can effectively assist clinicians in identifying high-risk patients and implementing personalized interventions, thereby improving patient outcomes.