Rough-Neutrosophic Evidence Lattices for Healthcare-Utilization
Stratification: Fusing Sleep and Wellness Indicators in the 2023
NPHA Dataset
Sajid Khan 1,∗, Arash Salehpour2
1Department of Computer Science, Sukkur IBA University, Sukkur, Pakistan
2Department of Cybersecurity, University of Istanbul, T¨urkiye
Emails: sajidkhan@iba-suk.edu.pk; arashsalehpour@halic.edu.tr
Abstract
Healthcare-utilization prediction from survey data is mathematically difficult because the observable variables are categorical,
self-reported, and partially discordant. A respondent may report poor physical health but no sleep disruption, or
regular sleep-medication use with favorable mental-health ratings. Such cases are not well represented by classifiers that
collapse all evidence into a single likelihood vector. This paper proposes a rough-neutrosophic evidence-lattice model for
stratifying older adults according to the number of doctors visited in a year. The model maps categorical sleep and wellness
indicators into single-valued neutrosophic triples, estimates entropy-based evidence weights, introduces a rough boundary
term from local equivalence classes, and ranks each respondent using an indeterminacy-penalized decision functional. The
method is evaluated using the 2023 UCI National Poll on Healthy Aging schema and a reproducible computational implementation.
The results show that the proposed lattice-based formulation improves macro-F1 over conventional categorical
baselines while preserving interpretable truth, falsity, and indeterminacy degrees for each utilization class.
Keywords: Single-valued neutrosophic set; Rough set; Information fusion; Healthcare-utilization
stratification; Entropy weighting; Categorical evidence