A Neutrosophic Recommender System for Medical Diagnosis Based on Algebraic Neutrosophic Measures

TL;DR

This paper introduces a neutrosophic recommender system for medical diagnosis that leverages algebraic neutrosophic measures to handle uncertainty and improve prediction accuracy, validated through experiments on multiple datasets.

Contribution

It proposes a novel neutrosophic recommender system with algebraic operations and similarity measures, and develops a new diagnosis algorithm validated by extensive experiments.

Findings

The proposed algorithm outperforms existing methods in accuracy and efficiency.

Algebraic structures effectively model uncertainty in medical diagnosis.

Experimental results demonstrate the method's robustness across various datasets.

Abstract

Neutrosophic set has the ability to handle uncertain, incomplete, inconsistent, indeterminate information in a more accurate way. In this paper, we proposed a neutrosophic recommender system to predict the diseases based on neutrosophic set which includes single-criterion neutrosophic recommender system (SC-NRS) and multi-criterion neutrosophic recommender system (MC-NRS). Further, we investigated some algebraic operations of neutrosophic recommender system such as union, complement, intersection, probabilistic sum, bold sum, bold intersection, bounded difference, symmetric difference, convex linear sum of min and max operators, Cartesian product, associativity, commutativity and distributive. Based on these operations, we studied the algebraic structures such as lattices, Kleen algebra, de Morgan algebra, Brouwerian algebra, BCK algebra, Stone algebra and MV algebra. In addition, we introduced several types of similarity measures based on these algebraic operations and studied some of their theoretic properties. Moreover, we accomplished a prediction formula using the proposed algebraic similarity measure. We also proposed a new algorithm for medical diagnosis based on neutrosophic recommender system. Finally to check the validity of the proposed methodology, we made experiments on the datasets Heart, RHC, Breast cancer, Diabetes and DMD. At the end, we presented the MSE and computational time by comparing the proposed algorithm with the relevant ones such as ICSM, DSM, CARE, CFMD, as well as other variants namely Variant 67, Variant 69, and Varian 71 both in tabular and graphical form to analyze the efficiency and accuracy. Finally we analyzed the strength of all 8 algorithms by ANOVA statistical tool.