Artificial Neural Network Parameter Tuning Framework For Heart Disease Classification

被引：0

作者：

Abu Yazid, M. Haider ^{[1
]}

Satria, Muhammad Haikal ^{[2
]}

Talib, Shukor ^{[1
]}

Azman, Novi ^{[3
]}

机构：

[1] UTM, Fac Comp, Johor Baharu, Malaysia

[2] UTM, Fac Biosci & Med Engn, Johor Baharu, Malaysia

[3] Univ Nas, Fac Sci & Engn, Jakarta, Indonesia

来源：

2018 5TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING, COMPUTER SCIENCE AND INFORMATICS (EECSI 2018) | 2018年

关键词：

artificial neural network; heart disease classification; artificial neural network parameter tuning; statlog heart dataset; cleveland heart dataset; IMMUNE RECOGNITION SYSTEM; DIAGNOSIS; FAILURE; RISK;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Heart Disease are among the leading cause of death worldwide. The application of artificial neural network as decision support tool for heart disease detection. However, artificial neural network required multitude of parameter setting in order to find the optimum parameter setting that produce the best performance. This paper proposed the parameter tuning framework for artificial neural network. Statlog heart disease dataset and Cleveland heart disease dataset is used to evaluate the performance of the proposed framework. The results show that the proposed framework able to produce high classification accuracy where the overall classification accuracy for Cleveland dataset is 90.9% and 90% for Statlog dataset.

引用

页码：674 / 679

页数：6

共 29 条

[21] Diagnosis of heart disease using artificial immune recognition system and fuzzy weighted pre-processing [J].