The influence of different factors on the bond strength of lithium disilicate-reinforced glass-ceramics to Resin: a machine learning analysis.

Background: To assess the influence of various factors on the bond strength of glass-based ceramics and develop a model that can predict the bond strength values using machine learning (ML).

Methods: The bond strength values of lithium disilicate-reinforced glass-ceramics were collected from existing literature. Nineteen features were listed, and 9 ML algorithms, including logistic regression, k-nearest neighbors, support vector machine, decision tree, ensemble methods (extra trees, random forest, gradient boosting, and extreme gradient boosting), and multilayer perceptron, were employed.