Identifying Critical Pathways in Coronary Heart Disease via Fuzzy Subgraph Connectivity
This work addresses the challenge of interpreting complex, uncertain relationships in CHD for clinical decision-making, but it appears incremental as it applies an existing fuzzy graph method to a specific medical domain.
The paper tackled the problem of modeling uncertainty in coronary heart disease (CHD) risk prediction by constructing a fuzzy graph and using fuzzy subgraph connectivity (FSC) to identify critical pathways, such as diagnostic routes and risk factors, with results showing it bounds connectivity and reveals edges that reduce predictive strength when removed.
Coronary heart disease (CHD) arises from complex interactions among uncontrollable factors, controllable lifestyle factors, and clinical indicators, where relationships are often uncertain. Fuzzy subgraph connectivity (FSC) provides a systematic tool to capture such imprecision by quantifying the strength of association between vertices and subgraphs in fuzzy graphs. In this work, a fuzzy CHD graph is constructed with vertices for uncontrollable, controllable, and indicator components, and edges weighted by fuzzy memberships. Using FSC, we evaluate connectivity to identify strongest diagnostic routes, dominant risk factors, and critical bridges. Results show that FSC highlights influential pathways, bounds connectivity between weakest and strongest correlations, and reveals critical edges whose removal reduces predictive strength. Thus, FSC offers an interpretable and robust framework for modeling uncertainty in CHD risk prediction and supporting clinical decision-making.