Heart Disease Prediction Using Integer-Coded Genetic Algorithm (ICGA) Based Particle Clonal Neural Network (ICGA-PCNN)
Nowadays, cardiovascular diseases are known as the one of the most dangerous and common problems in modern society. Analyzing and classifying the ECG signal will yield an accurate detection of different arrhythmias. Hence this paper proposes an Integer-Coded Genetic Algorithm (ICGA) based Particle Clonal Neural Network (ICGA-PCNN) for classifying the different ECG arrhythmias. Initially the histogram features and morphological features are extracted from the Pan-Tompkins based QRS complex. After that the optimal set of features has been selected using the ICGA for the extracted features. Then Multilayer feed forward neural network (MFNN) is used as a classifier to classify the ECG signal, where the weight and the biased are trained using the Particle based clonal selection. The MIT-BIH arrhythmias ECG Database has been presented as a database to train and test the proposed ICGA-PCNN classifier. The experimental results show that the proposed classifier approach performs better than the existing classification approaches in terms of classification accuracy, sensitivity and specificity.
Keywords: ECG, Cardiovascular Diseases, Pan-Tompkins, ICGA-PCNN, Integer-Coded Genetic Algorithm, MFNN, Particle Swarm Optimization, Clonal Selection.
Volume: 8 | Issue: 2
Issue Date: April , 2018