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

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...

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Bibliographic Details
Published in:Advances in artificial neural systems 2014-01, Vol.2014, p.1-11
Main Authors: Majeed Alneamy, Jamal Salahaldeen, Hameed Alnaish, Rahma Abdulwahid
Format: Article
Language:English
Subjects:
Back propagation
Cardiovascular disease
Classification
Fuzzy logic
Heart
Mathematical models
Medical diagnosis
Methods
Neural networks
Optimization techniques
Population
Studies
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Summary: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 K-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.
ISSN:1687-7594
1687-7608
DOI:10.1155/2014/796323