基于因果特征学习的有权同构图分类算法

doi:10.13229/j.cnki.jdxbgxb.20230384

Abstract

Abstract:

Aiming at the problem that the existing neural network methods are not accurate enough to predict the Killip classification of patients with myocardial infarction， a weighted isomorphic graph classification algorithm that can learn causal features is proposed. Use the differentiated learning objectives to separate the graph-level representation features of causal correlation and non-causal correlation， and then use the backdoor adjustment method in causal reasoning to reduce the confusion of non-causal features on classification results. The experimental results show that the average accuracy of the method proposed in this paper reaches 80.52% in the prediction task of the Killip grading of patients with myocardial infarction， which is 3.72% higher than that of the non-graph neural network method， and 0.72% higher than that of the graph neural network method without learning causal characteristics. Therefore， the method proposed can better complete the task of predicting the Killip grading of patients with myocardial infarction.

Key words: computer application technology, graph neural network, causal analysis, Killip classification

CLC Number:

TP391.4

Xiang-jiu CHE,Yu-ning WU,Quan-le LIU. A weighted isomorphic graph classification algorithm based on causal feature learning[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(2): 681-686.

Figures/Tables 5

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References 12

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模型	准确率/%	召回率/%
MLP	76.80	29.19
GCN	79.80	27.88
CGCN	80.52	32.83

A weighted isomorphic graph classification algorithm based on causal feature learning

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