THE ESHONKULOV HYBRID ALGORITHM BASED ON MINIMAL SPANNING TREES AND MULTIDIMENSIONAL CLINICAL SYNDROME MATRICES FOR ADAPTIVE SCREENING OF LATENT MUTATIONS

This paper presents the theoretical foundations and clinical validation of the newly developed Eshonkulov Hybrid Algorithm, engineered specifically for high-precision digital screening of latent mutations and viral hepatitis co-inflictions (HBV/HDV). The Eshonkulov Algorithm strategically integrates the graph theory principles of Minimal Spanning Trees (MST) via Prim's theorem with an adaptive boundary threshold mechanism (lambda = 30.0). By operating directly on orthogonal clinical syndrome matrices—cytolytic (X1), cholestatic (X2), and mesenchymal-inflammatory (X3)—the proposed approach dynamically isolates anomalous mutation vectors. Experimental benchmarks demonstrate that the Eshonkulov Algorithm reduces algorithmic computational complexity by 3.8 times compared to traditional K-Means clustering, generating a 68% reduction in mass molecular PCR genotyping costs while maintaining 100% diagnostic sensitivity. This establishes a sovereign mathematical asset for regional healthcare systems.