A Hybrid ANN-GWO Algorithm for Prediction of Heart Disease

作     者：Hamza Turabieh Hamza Turabieh

作者机构：Information Technology Department CIT Collage Taif University Taif KSA 

出 版 物：《American Journal of Operations Research》 (美国运筹学期刊（英文）)

年 卷 期：2016年第6卷第2期

页      面：136-146页

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

主　　题：Artificial Neural Network Gray Wolf Optimizer Back-Propagation Heart Disease 

摘      要：The paper investigates the powerful of hybridizing two computational intelligence methods viz., Gray Wolf Optimization (GWO) and Artificial Neural Networks (ANN) for prediction of heart disease. Gray wolf optimization is a global search method while gradient-based back propagation method is a local search one. The proposed algorithm implies the ability of ANN to find a relationship between the input and the output variables while the stochastic search ability of GWO is used for finding the initial optimal weights and biases of the ANN to reduce the probability of ANN getting stuck at local minima and slowly converging to global optimum. For evaluation purpose, the performance of hybrid model (ANN-GWO) was compared with standard back-propagation neural network (BPNN) using Root Mean Square Error (RMSE). The results demonstrate that the proposed model increases the convergence speed and the accuracy of prediction.