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

ABSTRACT:

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. Importance analysis was performed to determine the significance of the 19 features. A new data set comprising the top five contributing features was used for bond strength class prediction. G

SUBMITTER: Liu J 

PROVIDER: S-EPMC11837651 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature