Developing Predictive Models for Periodontitis Progression Using Artificial Intelligence: A Longitudinal Cohort Study.

ABSTRACT:

Aim

To construct predictive models of periodontitis progression by applying Machine Learning (ML) to baseline data from a study of periodontitis progression.

Materials and methods

Logistic regression (LR), multi-layer perceptron (MLP) and probabilistic graphic model (PGM) were utilised on data from a multi-centre longitudinal study in which periodontally healthy (n = 113) and periodontitis participants (n = 302) were examined bi-monthly for 12 months without treatment. Periodontal examination was performed, and salivary levels of 10 analytes were determined. Clinical and demographic parameters and analytes levels were input into the model. The performance of 14 models was compared using the area under the receiver operating characteristic curve (AUROC), and feature importanc

SUBMITTER: Furquim CP 

PROVIDER: S-EPMC12420084 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature