Development and validation of a machine learning-based predictive model for secondary post-tonsillectomy hemorrhage.

ABSTRACT:

Background

The main obstacle to a patient's recovery following a tonsillectomy is complications, and bleeding is the most frequent culprit. Predicting post-tonsillectomy hemorrhage (PTH) allows for accurate identification of high-risk populations and the implementation of protective measures. Our study aimed to investigate how well machine learning models predict the risk of PTH.

Methods

Data were obtained from 520 patients who underwent a tonsillectomy at The 940th Hospital of Joint Logistics Support Force of Chinese People's Liberation Army. The age range of the patients was 2-57 years, and 364 (70%) were male. The prediction models were developed using five machine learning models: decision tree, support vector machine (SVM), extreme gradient boosting (XGBoost), random fo

SUBMITTER: Hu X 

PROVIDER: S-EPMC9941337 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature