An Artificial Immune System-Integrated Redox-Capacitive Biosensing Architecture for Dengue NS1 Detection: A Computational Proof of Concept

Dengue virus infection remains a major global public health concern, demanding rapid and reliable diagnostic tools. This work introduces a computational proof of concept for an intelligent bioanalytical architecture combining a redox-capacitive biosensing platform with an Artificial Immune System (AIS) machine learning framework to detect the Dengue NS1 protein. Using a simulated dataset of 32 independent sample profiles across 49 frequency points, the AIS algorithm was systematically evaluated on isolated real (C′) and imaginary (C″) capacitance components under distinct operational scenarios. Utilizing real capacitance (C′), the AIS algorithm achieved 100.0% accuracy in discriminating positive NS1 profiles from controls and negative matrices, while dynamically adapting its Shape Affinity Limen parameter to maintain noise-resilience (83.3% accuracy) in the ambiguous Blank vs. Negative scenario. Statistical overlap analysis using the Bhattacharyya Index revealed that imaginary capacitance (C″) acts as a highly specific complementary marker. While C″ showed near-total structural overlap (0.995) between blank controls and negative samples—proving physical blindness to background matrix noise—it achieved flawless target identification (100.0% accuracy) for NS1 interaction. Backed by sub-millisecond algorithmic execution times, this dual-channel capacitive–computational framework provides a robust foundation for prospective real-time, point-of-care diagnostic tools.