Neutrosophic-Integrated Machine Learning Framework for Uncertainty-Aware Diagnosis and Decision Support in Dental Health

In the field of dental healthcare, diagnostic decisions are often hindered by vague symptoms, incomplete patient histories, and subjective clinical assessments. Traditional machine learning approaches, while effective in pattern recognition, struggle to handle the imprecision and indeterminacy inherent in real-world medical data. This paper proposes a novel hybrid framework that integrates Single-Valued Neu trosophic Sets (SVNS) with supervised machine learning algorithms to address uncertainty in dental diagnosis and treatment planning. The framework transforms raw clinical inputs into neutrosophic representations—capturing truthiness, indeterminacy, and falsity—thus enabling more nuanced feature modeling. These enriched features are then utilized to train classifiers such as Support Vector Machines, Decision Trees, and Neural Networks for accurate disease identification and treatment recommendation. The proposed model is validated using real and synthetic dental datasets, and its performance is benchmarked against conventional fuzzy and crisp decision models. Experimental results demonstrate that the neutrosophic-augmented machine learning framework achieves higher diagnostic accuracy, better uncertainty handling, and enhanced interpretability. This research provides a significant step toward the development of intelligent, uncertainty-aware decision support systems for dental health practitioners.