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科技导报
|专题：人工智能
2018
, 36
(17) :
91
-96
T-S模糊神经网络在冠心病智能诊断中的应用
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刘铭1 , 聂磊1 , 周芷茜2
作者信息
1. 长春工业大学数学与统计学院, 长春 130012;
作者简介:
刘铭,副教授,研究方向为智能计算与数据挖掘,电子信箱:jlcclm@163.com
Application of T-S fuzzy neural network to intelligent diagnosis of coronary heart disease
LIU Ming1 , NIE Lei1 , ZHOU Zhiqian2
Affiliations
1. School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China;
出版时间: 2018-09-13
doi: 10.3981/j.issn.1000-7857.2018.17.011
文章导航
冠心病是一种最为常见的心血管疾病，近年来在中国的发病率与死亡率逐年升高，准确诊断和及时治疗是有效降低冠心病死亡率的主要措施。通过引入模糊系统的理论，在传统BP神经网络结构中加入了模糊层与模糊规则计算层，建立了T-S模糊神经网络模型。使用该模型对采集于克利夫兰诊所的297组数据进行分析预测，与传统BP神经网络预测结果对比显示，T-S模糊神经网络模型的平均准确率为82.93%，而传统BP神经网络的平均准确率为75.56%，表明T-S模糊神经网络模型在冠心病的智能诊断中具有较高的预测准确率。
冠心病
/
模糊系统
/
BP神经网络
/
T-S模糊神经网络
Coronary heart disease is one of the most common cardiovascular diseases. In recent years, the incidence and mortality of coronary heart disease in China have increased year by year. Accurate diagnosis and timely treatment are the main measures to effectively reduce the mortality of coronary heart disease. With the help of the fuzzy system theory and by adding a fuzzy layer and fuzzy rule calculation layer to the structure of a traditional BP neural network, a T-S fuzzy neural network model is established in this paper. Using this model, the 297 data sets of coronary heart disease collected from the Cleveland Clinic are analyzed for diagnostic prediction. The average accuracy of the fuzzy neural network model reaches 82.93%, which is higher than 75.56%, the average accuracy of the traditional BP neural network in intelligent diagnosis of coronary heart disease.
coronary heart disease
/
fuzzy system
/
BP neural network
/
T-S fuzzy neural network
刘铭, 聂磊, 周芷茜.
T-S模糊神经网络在冠心病智能诊断中的应用.
科技导报,
2018
, 36
(17)
: 91
-96
.
DOI: 10.3981/j.issn.1000-7857.2018.17.011
LIU Ming, NIE Lei, ZHOU Zhiqian.
Application of T-S fuzzy neural network to intelligent diagnosis of coronary heart disease[J].
Science & Technology Review ,
2018
, 36
(17)
: 91
-96
.
DOI: 10.3981/j.issn.1000-7857.2018.17.011
2018年第36卷第17期
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文章信息
doi: 10.3981/j.issn.1000-7857.2018.17.011
接收时间：2018-06-03
首发时间：2018-09-18
出版时间：2018-09-13
收稿日期：2018-06-03
修回日期：2018-08-30
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2018.17.011
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2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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