Myocardial-Infraction Based on Intelligent Techniques

Abstract

Problem statement: Heart disease is the leading cause of death in the world. One of the most common types of heart diseases is Myocardial-Infraction. The ability to automatically identify Myocardial-Infraction from cardiac profiles test is important for clinical diagnosis and treatment. Approach: The aim of this study is to see whether the heart is healthy or not, in another words to verify the presence or the absence of Myocardial-Infraction using selected classification methods (neural networks and support vector machines) and to compare the performance of these used methods. Input layer, consists of input features (measurement of labs. Testing for CK, GOT, LDH, Tr), hidden layer of 2 neurons, output layer of one neuron activated by logistic function, which determine if heart is healthy or not. Result: The classification showed 97% for SVM-nonlinear classifier using radial function, 57% for SVM-linear classifier and 90% for Neural Networks (Back Propagation). Conclusion: The result strongly suggested that nonlinear SVMs can aid in the diagnosis of Myocardial-infraction.