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

Abstract

 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, strug
gle 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 un
certainty 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 recommenda
tion. 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 learn
ing framework achieves higher diagnostic accuracy, better uncertainty handling, and
 enhanced interpretability. This research provides a significant step toward the devel
opment of intelligent, uncertainty-aware decision support systems for dental health
 practitioners

DOI: 10.5281/zenodo.16708889