Utilizing Artificial Intelligence in Predicting Periodontal Disease Progression: A Machine Learning Approach

Abstract

Background: Periodontal disease is a significant public health concern, affecting a large percentage of the global population and often leading to tooth loss. Objective: This study evaluates the efficacy of machine learning algorithms in predicting the progression of periodontal disease based on clinical and demographic data. Methods: A cohort study involving 500 participants was conducted, utilizing demographic data, clinical parameters, and medical histories to train various machine learning models. Results: The Random Forest algorithm demonstrated the highest predictive accuracy (87%) for disease progression. Conclusions: Implementing AI-driven methodologies in periodontal diagnostics can enhance early intervention strategies, thereby reducing the incidence of severe periodontal outcomes. These findings suggest that integrating artificial intelligence tools in routine dental practice could optimize patient management.