基于贝叶斯网络的新型细菌性肺炎诊断模型设计与实现

Design and implementation of a new diagnosis model for bacterial pneumonia based on Bayesian network

近年来,由于环境污染、饮食不健康等因素,越来越多的人感染上细菌性肺炎,它给人类生命带来了巨大的威胁。为了有效预防感染,保障人民生命安全,急需一种能有效诊断肺炎的方法。传统的CT图像检测费时费力,成本较高,且肺部CT影像扫描存在误诊的情况,效率不高。为了有效解决上述问题,提高细菌性肺炎的诊断准确率,减轻医护人员的工作压力,文章提出一种基于贝叶斯网络的新型肺炎诊断模型,该模型基于Matlab编写,并经过了严格的仿真测试,最终达到了约85%的诊断准确率。

In recent years,due to environmental pollution,unhealthy diet and other factors,more and more people are infected with bacterial pneumonia,which poses a great threat to human life.In order to effectively prevent infection and ensure people's life safety,it is urgent to find an effective met hod to diagnose pneumonia.The traditional CT image detection is time-consuming,laborious and costly,and the lung CT image scanning has the situation of misdiagnosis,and the efficiency is not high.In order to effectively solve the above problems,improve the diagnostic accuracy of bacterial pneumonia,and reduce the work pressure of medical staff,this paper proposes a new diagnosis model of pneumonia based on Bayesian network.The model is compiled based on Matlab,and after strict simulation testing,it finally reaches about 85%of the diagnostic accuracy.