Machine learning models for prognosis prediction in regenerative endodontic procedures.

ABSTRACT:

Background

This study aimed to establish and validate machine learning (ML) models to predict the prognosis of regenerative endodontic procedures (REPs) clinically, assisting clinicians in decision-making and avoiding treatment failure.

Methods

A total of 198 patients with 268 teeth were included for radiographic examination and measurement. Five Machine Learning (ML) models, including Random forest (RF), gradient boosting machine (GBM), extreme gradient boosting (XGB), Logistic regression (logR) and support vector machine (SVM) are implemented for the prediction on two datasets of follow-up periods of 1-year and 2-year, respectively. Using a stratified five folds of cross-validation method, each dataset is randomly divided into a training set and test set in a ratio of 8 :

SUBMITTER: Lu J 

PROVIDER: S-EPMC11827326 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature