Robust technological solutions for smart healthcare systems using an integrated Sugeno-Weber aggregation framework under q-fractional fuzzy information

This project offers a step‑by‑step framework for evaluating and choosing AI‑based smart healthcare systems using multi‑attribute decision‑making (MADM) methods. It combines q‑fractional fuzzy sets with Sugeno‑Weber aggregation operators to handle the uncertainty, vagueness, and subjective judgments that appear in clinical assessments. The framework compares five different AI‑driven healthcare systems against four key performance criteria: * Diagnostic accuracy * Response time * Implementation complexity * Cost‑to‑scalability trade‑offs Sugeno‑Weber operators have a parameter that lets users adjust how strongly the individual clinical values interact with each other. The q‑fractional fuzzy parameters broaden the way uncertain expert inputs can be represented. Together, these tools balance averaging and geometric effects, giving healthcare managers and

biomedical engineers a solid mathematical method for decision making. The project also includes practical guidance on implementation, algorithm design, and simulation support for building fuzzy decision‑support systems in clinical settings. Numerical simulations and sensitivity analyses show how changing the parameters alters the final ranking of the technology options, helping users make an objective choice of medical monitoring solutions.

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