aspg-39-4341-1792222300 20261017073140 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Neutrosophic and Information Fusion NIF 2836-7863 2025 5 1 Sajid Khan Department of Computer Science, Sukkur IBA University, Sukkur, Pakistan Arash Salehpour Department of Cybersecurity, Haliç University, Istanbul, Türkiye 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. 2025 26 31 4341 https://creativecommons.org/licenses/by/4.0/ 10.54216/NIF.050104 https://americaspg.com/journal/39/article/4341 https://americaspg.com/storage/articles/manuscripts/01M20T96KTC3DY5RFB0PS9MTVN.pdf