Diabetes insipidus (DI) is a common complication following endoscopic transsphenoidal surgery (TSS) for pituitary adenoma (PA), which affects the quality of life in patients. Therefore, there is a need to develop prediction models of postoperative DI specifically for patients who undergo endoscopic TSS. This study establishes and validates prediction models of DI after endoscopic TSS for PA patients using machine learning algorithms.We retrospectively collected PA patients who underwent endoscopic TSS in otorhinolaryngology and neurosurgery departments between January 2018 to December 2020. The patients were randomly split into a training set (70%) and a test set (30%). The four machine learning (ML) algorithms (logistic regression, random forest, support vector machine, and decision tree) were used to establish the prediction models. Area under the receiver operating characteristic curve were calculated to compare the performance of the models.A total of 232 patients were included, and 78 patients (33.6%) developed transient DI after surgery. Data were randomly divided into a training set (n = 162) and a test set (n = 70) for development and validation of the model, respectively. The Area under the receiver operating characteristic curve was highest in the random forest model (0.815), and lowest in the Logistic regression model (0.601). (Invasion of pituitary stalk was the most important feature for model performance, closely followed by macroadenomas, size classification of PA, tumor texture, and Hardy-Willson suprasellar grade.Machine learning algorithms identify preoperative features of importance, and reliably predict DI after endoscopic TSS for PA patients. Such a prediction model may enable clinicians to develop individualized treatment strategy and follow-up management.Copyright © 2023. Published by Elsevier Inc.