[1]黄 璐,毛晓艳.基于深度学习的诊断心脑血管疾病分类方法[J].计算机技术与发展,2021,31(增刊):36-40.[doi:10. 3969 / j. issn. 1673-629X. 2021. S. 007]
　HUANG Lu,MAO Xiao-yan.Classification Method of Cardiovascular and Cerebrovascular Diseases Based on Deep Learning[J].,2021,31(增刊):36-40.[doi:10. 3969 / j. issn. 1673-629X. 2021. S. 007]
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基于深度学习的诊断心脑血管疾病分类方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年增刊
页码:
36-40
栏目:
人工智能
出版日期:
2021-12-31

文章信息/Info

Title:
Classification Method of Cardiovascular and Cerebrovascular Diseases Based on Deep Learning
文章编号:
1673-629X(2021)S0036-05
作者:
黄 璐; 毛晓艳
北京控制工程研究所,北京 100094
Author(s):
HUANG Lu; MAO Xiao-yan
Beijing Institute of Control Engineering,Beijing 100094,China
关键词:
神经网络; 分类器; 深度学习; 特征分析
Keywords:
neural network; classifier; deep learning; characteristics analysis
分类号:
TP39
DOI:
10. 3969 / j. issn. 1673-629X. 2021. S. 007
摘要:
在人工智能与机器学习飞速发展的大环境下,考虑可以将其应用于医学研究领域和医疗行业。 在诊断心脑血管疾病时,可以使用深度学习方法,通过大数据挖掘协助医生进行分析判断与病情诊断。 文中采用支持向量机 SVM,集成学习算法 XGBoost 和 BP 神经网络三种方法进行对比,调节神经网络参数,训练分类器。 寻找最优的分类方式,使其达到最高的准确率,协助医生对病人进行心脑血管疾病的判断。 经数字仿真与模拟实验,进行数据挖掘与样本特征分析,调节参数、绘制散点图和特征相关性热力图分析,发现通过神经网络的方法训练分类器可以以较高的准确率应用于医学领域中。
Abstract:
In the context of the rapid development of artificial intelligence and machine learning,it can be considered to be applied to medical research and medical industry. In the diagnosis of cardiovascular and cerebrovascular diseases,deep learning can be used to assist doctors in analysis and diagnosis through big data mining. Three methods of support vector machine ( SVM ) , integrated learning algorithm XGBoost and BP neural network are used to compare,adjust the neural network parameters and train the classifier. To find the best classification method,so as to achieve the highest accuracy,to assist doctors in the diagnosis of patients with cardiovascular and cerebrovascular diseases. Through digital simulation and simulation experiments,data mining,sample feature analysis,parameter adjustment, scatter diagram drawing and feature correlation heat map analysis are carried out. It is found that the neural network training method can be applied to medical field with high accuracy.

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更新日期/Last Update: 2021-09-10