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[This article belongs to Volume - 71, Issue - 3]
Published on : 2026-03-29 18:46:27
Article Code: AMJ-29-03-2026-12370
Title : Advanced Hybrid Computational Intelligence Frameworks for Precision Medical Diagnosis: Integrating Deep Neuro-Fuzzy Systems with Quantum-Inspired Optimization
Author(s) : Dr. Nabil K Salman
Abstract :
The rapid evolution of artificial intelligence in healthcare necessitates sophisticated mathematical architectures
capable of processing heterogeneous medical data under uncertainty. This study presents a novel Hybrid Deep
Neuro-Fuzzy Inference System (HDNFIS) integrated with Quantum-Inspired Particle Swarm Optimization (QIPSO) for
the diagnosis of critical internal organ pathologies. Unlike conventional gradient projection methods, our framework
employs Hilbert-Schmidt Independence Criterion (HSIC) regularization within a Reproducing Kernel Hilbert Space
(RKHS) to capture non-linear dependencies between symptomatic variables. We introduce a Fractional-Order Fuzzy
Logic Controller (FOFLC) that utilizes Caputo fractional derivatives to model the memory effects in disease
progression. The proposed methodology was validated against four critical conditions: acute myocardial infarction,
septic shock (third degree), perforated peritonitis, and acute circulatory failure. Comparative analysis demonstrates
that the HDNFIS achieves diagnostic accuracy of 98.7%, outperforming traditional Bayesian networks (89.2%) and
standard fuzzy inference systems (92.4%). This research establishes a robust mathematical foundation for next
generation diagnostic expert systems, emphasizing the necessity of fractional calculus and quantum computing
principles in medical decision support systems.