Table of Contents
Advances in Artificial Neural Systems
Volume 2014 (2014), Article ID 796323, 11 pages
Research Article

Heart Disease Diagnosis Utilizing Hybrid Fuzzy Wavelet Neural Network and Teaching Learning Based Optimization Algorithm

Software Engineering Department, Computer and Mathematics Science College, University of Mosul, Mosul, Iraq

Received 16 May 2014; Revised 29 August 2014; Accepted 31 August 2014; Published 17 September 2014

Academic Editor: Chao-Ton Su

Copyright © 2014 Jamal Salahaldeen Majeed Alneamy and Rahma Abdulwahid Hameed Alnaish. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Among the various diseases that threaten human life is heart disease. This disease is considered to be one of the leading causes of death in the world. Actually, the medical diagnosis of heart disease is a complex task and must be made in an accurate manner. Therefore, a software has been developed based on advanced computer technologies to assist doctors in the diagnostic process. This paper intends to use the hybrid teaching learning based optimization (TLBO) algorithm and fuzzy wavelet neural network (FWNN) for heart disease diagnosis. The TLBO algorithm is applied to enhance performance of the FWNN. The hybrid TLBO algorithm with FWNN is used to classify the Cleveland heart disease dataset obtained from the University of California at Irvine (UCI) machine learning repository. The performance of the proposed method (TLBO_FWNN) is estimated using -fold cross validation based on mean square error (MSE), classification accuracy, and the execution time. The experimental results show that TLBO_FWNN has an effective performance for diagnosing heart disease with 90.29% accuracy and superior performance compared to other methods in the literature.