Artificial Intelligence for Predicting Toothaches: a Model for Proactive Dental Health Management

Abstract

This paper presents an artificial intelligence-based model for predicting toothache occurrences by analyzing various contributing factors such as age, brushing frequency, dietary habits, and genetic predispositions. Utilizing logistic regression, the model processes data from a dataset comprising individual dental health information, demonstrating its capability to classify patients at risk for toothache effectively. Evaluation metrics, including accuracy, confusion matrix, and ROC curve, indicate a promising level of predictive performance, underscoring the potential for AI to enhance preventive dental care. The findings suggest that integrating AI in dental health assessments could aid healthcare professionals in identifying high-risk individuals, promoting timely interventions, and ultimately improving patient outcomes in preventive dentistry.