목적: Mucoepidermoid carcinoma (MEC) is the most common type of malignancy originating from the parotid gland. The aim of this study was to develop and establish an effective survival prediction model based on a large population-based dataset of patients with advanced parotid gland MEC using a machine learning (ML) algorithm. 방법:A total of 607 patients with advanced T stage parotid gland MEC were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The dataset was randomly assigned to a train or test dataset at a 7:3 ratio. The Cox proportional hazard model (CoxPH), conditional survival forest model (CSF), random survival forest model (RSF), and DeepSurv model were used to predict disease-specific survival in patients with advanced parotid gland MEC. The predictive ability was evaluated using the concordance index (C-index) and the Integrated Brier Score (IBS). 결과:The best prediction performance was achieved with CSF (0.79) and DeepSurv (0.79) based on the C-index, and CSF (0.02) and CoxPH (0.02) based on IBS. In our survival prediction ML models, age, tumor size, and grade were important variables, as in the multivariate CoxPH model. 결론:We developed and compared ML-based survival prediction models with good performance in patients with advanced T-stage parotid gland MEC. Compared with conventional statistical methods, ML techniques have satisfactory results in predicting disease-specific survival in real- world data.