Recurrence Shadow Mapping under Neutrosophic Clinical
Evidence: An Uncertainty-Oriented Model for Post-Treatment
Healthcare Decision Support
Murodbek Ahrorov1,∗, Ahmed Aziz2
1Medical School, Central Asian University, Uzbekistan
2Central Asian University, Uzbekistan
Emails: m.akhrorov@centralasian.uz; aziz.bfci@gmail.com
Abstract
Post-treatment follow-up in differentiated thyroid cancer requires a decision model that is not limited to binary
recurrence prediction. Patients may present with partially reassuring anatomical findings, incomplete biochemical
response, hetero-geneous pathological subtype, or contradictory clinical history. These situations are better described
as a triadic state composed of support for recurrence, support against recurrence, and unresolved indeterminacy. This
paper proposes a recurrence shadow mapping model based on single-valued neutrosophic clinical evidence. The model
transforms clinico-pathologic descriptors into truth, indeterminacy, and falsity memberships; aggregates evidence
through entropy-contrast weighting; and produces a recurrence-shadow index that separates stable, observation, alert, and
high-alert follow-up states. The proposed method is designed for healthcare decision support rather than automatic
replacement of clinical judgment. Its mathematical contribution is a bounded neutrosophic score that penalizes
inconsistent evidence without suppressing clinically meaningful warning signals. Experimental evaluation demonstrates
that recurrence-oriented evidence sources can be expressed in a transparent mathematical form, and that indeterminacy
itself becomes an interpretable clinical quantity. The findings support the use of neutrosophic information fusion for
medical cases where uncertainty is structural rather than merely statistical.
Keywords: Neutrosophic healthcare decision support; Recurrence shadow; Differentiated thyroid cancer; Singlevalued
neutrosophic set; Clinical evidence fusion; Indeterminacy-aware risk stratification