Bonfring International Journal of Industrial Engineering and Management Science, Vol. 8, No. 2, April 2018 15 Abstract--- 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. I. INTRODUCTION ATA MINING is the process of extracting meaningful information from various resources which is used to analyze the decision about particular data. The mining process used in several applications such as prediction, emotion classification, hand gesture recognition, heart disease classification, iris recognition and cancer identification. From the above applications heart diseases are the most important, because heart diseases foremost cause of death. Heart disease factors can be split in two ways such as modifiable and non modifiable. Modifiable risk factors include obesity, smoking, lack of physical activity and so on. The non modifiable risk factors for heart disease are like age, gender, and family history [1]. Some kind of heart diseases are irregular heartbeat (arrhythmias), congenital heart defects, weak heart, muscles (cardiomyopathy), heart valve problems, heart infections and cardiovascular disease. Cardiovascular disease is the important cause of death. Cardiovascular disease raised blood pressure, S. Silvia Priscila, Research Scholar, Bharathiar University, Coimbatore, India. E-mail:sylviaprem2010@gmail.com Dr.M. Hemalatha, Research Supervisor, Bharathiar University, Coimbatore, India. E-mail:hema.bioinf@gmail.com DOI:10.9756/BIJIEMS.8394 peripheral artery disease, rheumatic heart disease, congenital heart disease and heart failure problems [2]. In the cardiovascular diseases may display various syndromes. By reason of this complexity, there is a need to detect diseases using diagnostic process. In the cardiovascular diseases may display various syndromes [3]. Hence, early detection is considerable to improve the diagnosis of cardiovascular diseases. As the symptoms are unsteady it is very challenging to the physicians or radiologists to identify abnormalities if they diagnose only by their experience. In EU country due to cardiovascular disease dearth percent in roughly is 45%, and pay hundred billion Euros for cardiovascular diseases [4]. Several cardiovascular disease cannot be find accurately by chest X-rays. The Cardiac abnormalities can be identified fast and efficiently using ECG signals. Thus, it is important to develop a fully automatic technique with high sensitivity to assist the early detection of cardiovascular disease with ECG. In our proposed based Particle Clonal Neural Network (ICGA-PCNN) for classifying the different ECG arrhythmias such as Normal, LBBB, APB, VE and VF to aid in diagnosis. The rest of the paper is organized as follows: Section 2 describes the recent related work of automated diagnosis of ECG signals. Section 3 describes the methods and materials. The experimental results are discussed in the section 4. Finally, section 5 renders the conclusion. II. RELATED WORK Lovepreet Kaur [5] proposed prototype Intelligent Heart Disease Prediction System with Fuzzy C Means Clustering algorithm. An intelligent Heart Disease Prediction System built with the help of miming technique like decision trees, naive bayes and neural network. The proposed system using data acquisition, pre-processing, feature extraction, and classification methods which is used to analysis the heart diseases in terms of accuracy, time, specificity and sensitivity, and to make intelligent medical decisions from the traditional decision support system. This paper have58 records and 14 attributes are used to predict the heart diseases. The prediction system displays 86.6% accuracy, 32 milliseconds time, 0.44 specificity and 0.45 sensitivity. Chaitrali S. Dangare et al [6]., proposed mining techniques such as decision trees, naive bayes and neural networks, in which to classify the heart diseases from the historical heart database based on Quality of Service (QoS). Based on the Quality of Service provides correct and effective treatment. In this paper takes two input attributes from 13 input attributes Heart Disease Prediction Using Integer-Coded Genetic Algorithm (ICGA) Based Particle Clonal Neural Network (ICGA-PCNN) S. Silvia Priscila and Dr.M. Hemalatha D ISSN 2277-5056 | © 2018 Bonfring