Development and validation of machine learning models for esophagogastric variceal bleeding risk in hepatocellular carcinoma patients

BACKGROUNDEsophagogastric variceal bleeding (EGVB) is a common and highly fatal complication in patients with hepatocellular carcinoma (HCC).In this study, our aim was to develop a predictive model to assess the risk of EGVB in patients with HCC. AIMTo construct and internally validate a machine learning model to predict the risk of EGVB in patients with HCC. METHODSThis study included 188 patients with HCC, who were randomly assigned to the training set and validation set in a 7:3 ratio.Using LASSO regression and multivariate Logistic regression, the variables significantly associated with EGVB were identified, and six machine learning models were constructed using these variables.The predictive performance of different models was compared using metrics such as the area under the curve (AUC).The optimal model was further evaluated, and the SHapley Additive exPlanation (SHAP) interpretability analysis was performed. RESULTSIn the end, four characteristic variables, namely albumin, splenic vein diameter, tumor burden score, and ascites, were selected.These four variables were used to build six machine learning models.Among these, the support vector machine (SVM) achieved the highest AUC (0.931), F1 score (0.889), Youden index (0.785), and sensitivity (0.889) across all six models.Based on the overall performance, SVM was identified as the optimal model.Internal validation demonstrated that the SVM model had good calibration performance and clinical applicability.SHAP interpretability analysis further visualized the contribution pathways of each variable through a feature 3 / 34 importance bar chart, a swarm plot, dependence plots, and a waterfall plot, providing interpretable evidence for clinical decision-making. CONCLUSIONThe successful development of a predictive model for EGVB in HCC patients, along with SHAP analysis, can help clinicians identify high-risk individuals at an early stage and implement personalized interventions.