Identifying Critical Pathways in Coronary Heart Disease via Fuzzy Subgraph Connectivity

Abstract

Coronary heart disease (CHD) involves complex interactions among uncontrollable factors, controllable lifestyle factors, and clinical indicators, where relationships are inherently uncertain and imprecise. Traditional risk assessment models assume precise probabilistic relationships that may not capture the ambiguity in medical data. This paper introduces fuzzy subgraph connectivity (FSC) as a systematic framework for modeling uncertainty in CHD risk prediction. We construct a fuzzy graph where vertices represent risk factors and clinical indicators, with edge weights quantifying the strength of their associations. FSC measures connectivity between subgraphs to identify the strongest diagnostic pathways, dominant risk factors, and critical bridges whose removal significantly impacts predictive accuracy. Theoretical results establish bounds on connectivity and characterize conditions for maximum inter-subgraph relationships. Application to a CHD model demonstrates that FSC reveals clinically meaningful pathways, quantifies the relative influence of controllable versus uncontrollable factors, and provides interpretable insights for targeted interventions. This approach complements existing risk models by offering a transparent, graph-based framework for uncertainty quantification in cardiovascular risk assessment.