Neutrosophic-Supported Machine Learning Models for Oral Disease Classification

Authors

  • Ibrahim M. Elezmazy Zagazig University, Faculty of Computers and Informatics, Computer Science, Egypt
  • Doaa El-Shahat Zagazig University, Faculty of Computers and Informatics, Computer Science, Egypt

Keywords:

Neutrosophic domain; Machine learning; Oral diseases; Image classification.

Abstract

This study is presented to investigate the influence of the neutrosophic (NS) domain on the performance 
of the most common machine learning (ML) models. Specifically, it evaluates the effectiveness of 
Random Forest (RF), Extra Trees (ET), K-Neighbors (KNN), Gaussian Naive Bayes (GaussianNB), and 
Decision Tree (DT) classifiers in detecting oral diseases. The NS domain divides any image into three 
membership components: falsity (

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Published

2025-02-10

Issue

Vol. 79 (2025): Neutrosophic Sets and Systems

Section

Article

License

Copyright (c) 2025 Neutrosophic Sets and Systems

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

 Creative Commons Attribution (CC BY) license.

How to Cite

Ibrahim M. Elezmazy, & Doaa El-Shahat. (2025). Neutrosophic-Supported Machine Learning Models for Oral Disease Classification . Neutrosophic Sets and Systems, 79, 657-668. https://fs.unm.edu/nss8/index.php/111/article/view/5656