Correlation Measure for Quadri-Partitioned Neutrosophic Refined Sets and its Application in Medical Diagnosis

Abstract

This work introduces a new correlation measure of quadri-partitioned neutrosophic refined sets (QPNRS) to address the problem of ambiguity and bias that may exist in the medical diagnostic. The model is a patient-symptom-disease interaction model that uses four membership functions—truth, relative truth, relative falsehood, and falsehood—to illustrate its interactions. Table III concludes the results by demonstrating that there is clear disease and estimator variance and that correlations between QPNRS measures Q and R are consistently strong and positive (0.743 -0.901) across four medical conditions. In particular, P2 is more extensively distributed, while P1 is constantly high in all environments, reaching its maximum value of 0.901 in the case of tuberculosis. These findings support the combined clinical use of Q and R by demonstrating a strong, primarily favorable reliance between them. We apply a multifaceted unsupervised learning pipeline to two distinct datasets—the conventional Iris dataset and patient disease-correlation profiles—in order to augment the correlation analysis. In both cases, the consistently strongly separated clusters support the process and imply that the detected groupings are not the result of labeling or modeling artifacts.