A Single-Valued Neutrosophic Weighted Aggregation Framework
for Multi-Attribute Heart Disease Risk Assessment: An
Information Fusion Perspective
Jeong Chan Park1,∗, Sajid Khan2
1Central Asian University, Uzbekistan
2Department of Computer Science, Sukkur IBA University, Sukkur, Pakistan
Emails: goodnews1979@gmail.com; sajidkhan@iba-suk.edu.pk
Abstract
Reliable early detection of cardiovascular disease requires integrating multiple clinical indicators under conditions of
uncertainty, partial measurement, and inconsistent expert knowledge. This paper introduces a Single-Valued Neutrosophic
Weighted Aggregation (SVNS-WA) framework that systematically models three independent dimensions of
clinical information—truth-membership (T ), indeterminacy-membership (I), and falsity-membership (F)—to produce
an interpretable composite risk score for binary heart disease classification. Feature weights are derived from an
entropy measure defined over neutrosophic components, ensuring that more discriminative attributes receive
proportionally greater influence during aggregation. A score function S(x) = (2 + Tagg − Iagg −Fagg)/3 maps each
aggregated neutro-sophic value to the unit interval, and an optimal decision threshold is identified via Youden’s J
statistic. Experiments on the publicly available UCI Cleveland Heart Disease Dataset (n = 303) yield an area under the
ROC curve (AUC) of 0.765 and a sensitivity of 83.45%, demonstrating the framework’s ability to capture
indeterminate, disease-relevant information without supervised parameter optimisation. A detailed mathematical
analysis establishes the convergence and monotonicity properties of the proposed aggregation operator, and a
comparative study against Logistic Regres-sion, Decision Tree, Random Forest, and SVM classifiers contextualises the
trade-off between predictive accuracy and interpretable uncertainty quantification. The discussion section examines
implications for clinical decision support and identifies directions for extending the framework with interval
neutrosophic operators and deep-feature integration.
Keywords: Neutrosophic sets; Single-valued neutrosophic sets; Information fusion; Weighted aggregation operator;
Medical diagnosis; Heart disease; Decision support; Entropy-based weighting; Uncertainty quantification