Risk Prediction Model of Cardiac Rupture Based on Weighted Bayesian Network
LIU Chuyang
YANG Xiang
CHEN Yanhong
Abstract：As the most fatal complication of acute myocardial infarction,the prediction and intervention of cardiac rupture af-ter myocardial infarction is particularly important.Because heart rupture is a rare disease with high mortality,its data set there is im-balance and data deletion,which makes it difficult for the deep learning model to achieve high accuracy,and the results of the mod-el need to be interpretable.In order to solve the above problems,this paper proposes a weighted Bayesian network model based on attention mechanism.The model builds a more accurate network structure by combining medical knowledge and algorithms.Second-ly,by integrating attention weight into Bayesian network,more attention can be paid to significance indicators,and the accuracy and interpretability of the model are enhanced.Finally,on the real data,the risk of cardiac rupture after acute myocardial infarction is evaluated.The experimental results show that the accuracy and interpretability of the model are better,and its F1 score and AUC value can reach 0.771 8 and 0.798 7 respectively.
Keywords：disease predictionelectronic health recordattention mechanismBayesian networkcardiac rupture
Publication Date：2025-03-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 684-691 )