Mathematical and AI-Based Predictive Modelling for Dental Caries Risk Using Clinical and Behavioural Parameters

TL;DR

This paper presents a new model combining math and AI to predict dental caries risk based on factors like sugar intake and oral hygiene.

Contribution

A novel hybrid model integrating mathematical and AI methods for personalized dental caries risk prediction.

Findings

The hybrid model achieved 91.2% accuracy in classifying caries risk levels.

Sugar intake and oral hygiene were identified as the most influential risk factors.

Fluoride exposure and salivary pH showed protective effects against caries.

Abstract

Dental caries remains one of the most prevalent chronic diseases worldwide, driven by complex interactions among dietary, hygienic, and biological factors. This study introduces a hybrid predictive framework that integrates mathematical modelling and artificial intelligence (AI) to estimate individual caries risk based on daily sugar intake, oral hygiene index, salivary pH, fluoride exposure, age, and sex. A first-order balance differential equation was applied to simulate demineralisation–remineralisation dynamics, while a feed-forward artificial neural network (ANN) was trained on simulated and literature-derived datasets. The hybrid model demonstrated strong predictive performance, achieving 91.2% accuracy and an AUC of 0.98 in classifying individuals into low-, moderate-, and high-risk categories. Sensitivity analysis identified sugar intake and oral hygiene as dominant determinants, while fluoride and salivary pH showed protective effects. These findings highlight the feasibility of combining mechanistic and data-driven approaches to enhance early risk assessment and support the development of intelligent, personalised screening tools in preventive dentistry.

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