Diagnosis of heart disease using genetic algorithm based trained recurrent fuzzy neural networks

被引：81

作者：

Uyar, Kaan ^{[1
]}

Ilhan, Ahmet ^{[1
]}

机构：

[1] Near East Univ, POB 99138,Mersin 10, Nicosia, Trnc, Turkey

来源：

9TH INTERNATIONAL CONFERENCE ON THEORY AND APPLICATION OF SOFT COMPUTING, COMPUTING WITH WORDS AND PERCEPTION, ICSCCW 2017 | 2017年 / 120卷

关键词：

Heart disease; recurrent fuzzy neural networks; SYSTEM; EXTRACTION; MODEL;

D O I：

10.1016/j.procs.2017.11.283

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The World Health Organization (WHO) estimated one third of all global deaths reason as cardiovascular diseases in 2015. Some computational techniques were proposed for investigation of heart diseases. This study proposes a genetic algorithm (GA) based trained recurrent fuzzy neural networks (RFNN) to diagnosis of heart diseases. The University of California Irvine (UCI) Cleveland heart disease dataset is used in this study. Out of total 297 instances of patient data, 252 are used for training and 45 of them are chosen to be the testing. The results showed that 97.78% accuracy was obtained from testing set. In addition to the accuracy, root mean square error, the probability of the misclassification error, specificity, sensitivity, precision and F-score are calculated. The results were found to be satisfying based on comparison. (c) 2018 The Authors. Published by Elsevier B.V.

引用

页码：588 / 593

页数：6

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