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 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. 1. Introduction Heart disease is a term that refers to any disturbance that makes the heart function abnormally [1]. When the coronary arteries are narrowed or blocked, the blood flow to the myocardium is decreased. This represents the main reason for the emergence of heart disease in humans. There are several risk factors for this disease, including diabetes, smoking, obesity, and a family history of heart disease, high cholesterol, and high blood pressure [1–3]. Actually, the incidence rate of heart disease is on the rise. Every year about 720,000 Americans have a heart attack. Among them, 515,000 have their first heart attack and 205,000 people have a second (or third, etc.) heart attack. Heart disease causes the death of about 600,000 people in the United States every year, which makes it responsible for one in every four deaths [4]. Due to the large number of patients with heart disease, it became necessary to have a powerful tool that works accurately and efficiently for diagnosing this disease and helping the physicians make decisions about their patients. This is because the process of diagnosis and decision making is a difficult task and needs a lot of experience and skill. Recently, a lot of research has been published regarding the field of medical diagnosis of heart disease. In 2008, Kahramanli and Allahverdi designed a hybrid system that represents a combination between fuzzy neural network (FNN) and