Heart Disease Classification Using Neural Network and Feature Selection

In this study, we introduces a classification approach using Multi-Layer Perceptron (MLP)with Back-Propagation learning algorithm and a feature selection algorithm along with biomedical test values to diagnose heart disease. Clinical diagnosis is done mostly by doctor´s expertise and experience. But still cases are reported of wrong diagnosis and treatment. Patients are asked to take number of tests for diagnosis. In many cases, not all the tests contribute towards effective diagnosis of a disease. Our work is to classify the presence of heart disease with reduced number of attributes. Original, 13 attributes are involved in classify the heart disease. We use Information Gain to determine the attributes which reduces the number of attributes which is need to be taken from patients. The Artificial neural networks is used to classify the diagnosis of patients. Thirteen attributes are reduced to 8 attributes. The accuracy differs between 13 features and 8 features in training data set is 1.1% and in the validation data set is 0.82%.