Flower Pollination Neural Network For Heart Disease Classification

Joint Conference on Green Engineering Technology & Applied Computing 2019

工业技术（总论）;计算机科学

Haider Bin Abu Yazid, Mohamad^1 ; Shukor Talib, Mohamad^1 ; Haikal Satria, Muhammad^2

School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81300 UTM, Johor Bahru, Malaysia^1

School of Bioscience and Medical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81300 UTM, Johor Bahru, Malaysia^2

关键词: Back propagation neural networks;    Classification accuracy;    Decision support tools;    Error minimization;    Heart disease;    Local minimums;    UCI machine learning repository;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/551/1/012072/pdf
DOI  :  10.1088/1757-899X/551/1/012072

来源: IOP

【 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 have been previously proposed. However, artificial neural network using conventional back propagation algorithm for error minimization and these algorithm tend to stuck at local minima. This paper proposed the use of flower pollination algorithm as a substitute to conventional back propagation algorithm for error minimization. Heart disease dataset obtain from UCI machine learning repository is used to evaluate the performance of the proposed framework. The results show that the proposed flower pollination neural network able to produce higher classification accuracy compared to the conventional back propagation neural network algorithm.