Abstract
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.
Highlights
Heart disease is a term that refers to any disturbance that makes the heart function abnormally [1]
The heart dataset is divided into 5 folds: one fold is used for testing and the other 4 folds are used for training
The teaching learning based optimization (TLBO) algorithm has been proposed for training fuzzy wavelet neural networks (FWNN)
Summary
Heart disease is a term that refers to any disturbance that makes the heart function abnormally [1]. Das et al in 2009 proposed a system for diagnosing heart disease by using the neural network ensemble model, which combines several individual neural networks that are trained on the same task This method increased the complexity and the execution time [3]. FWNN combines the main advantages of a fuzzy system, wavelet function, and neural networks and, could bring the low level learning and good computational ability of WNN into a fuzzy system and the high humanlike thinking of fuzzy system into the WNN [8]. In accordance with the above-mentioned advantages of both FWNN and TLBO, in this paper a new method (TLBOFWNN) is proposed to increase the efficiency of the heart disease diagnosis process. (ii) In the second layer, each node represents one fuzzy set, in which the calculation for the membership value of the input variable to the fuzzy set is carried out. (9) Repeat processes (3) to (5) until the termination condition is satisfied
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