用模糊图连接性识别冠心病关键风险路径。
Identifying Critical Pathways in Coronary Heart Disease via Fuzzy Subgraph Connectivity
- 构建包含不可控、可控因素和临床指标的模糊图，以隶属度加权边。
- 发现最强诊断路径和关键桥梁节点，移除后预测能力显著下降。
- 适合临床决策支持，尤其关注不确定关系建模的研究者。
冠心病（CHD）源于不可控因素、可控生活方式因素与临床指标间的复杂交互，其关系常具不确定性。本文构建了一个包含三类成分的模糊冠心病图，顶点代表各类因素，边权重由模糊隶属度决定。通过模糊子图连通性（FSC）评估连接强度，识别最强诊断路径、主导风险因子及关键桥梁。结果表明，FSC能突出影响性路径，界定最弱与最强关联之间的连通性范围，并揭示移除后会削弱预测能力的关键边。该方法为建模冠心病风险预测中的不确定性提供了可解释且稳健的框架，有助于临床决策。
原文摘要 · Abstract (English)
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.
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