Electrocardiogram (ECG), a non-invasive technique is used as a primary diagnostic tool for cardiovascular diseases. ECG provides valuable information about the functional aspects of the heart and cardiovascular system. The detection of cardiac arrhythmias in the ECG signal consists of detection of QRS complex in ECG signal; feature extraction from detected QRS complexes; classification of beats using extracted feature set from QRS complexes. Earlier methods have been developed by authors to predict heart disease on the basis of ECG but as each method has its own advantage as well disadvantage. Hence, in this thesis, the best training method i.e. Levenberg Marquardt algorithm has been utilized for classification on the basis of validation checks or epochs with an optimization technique. The purpose of this research work is to classify the disease dataset using Bacterial Foraging Optimization (BFO) Algorithm and trained by Levenberg Marquardt algorithm on the basis of the features extracted and also to test the image on the basis of the features at the database and the features extracted of the waveform, to be tested. The advantage of the proposed method is to minimize the error rate of the classification which occurs due to an insignificant count of R-peaks. The database from physionet.org has been used for performance analysis. Several experiments are performed on the test dataset and it is observed that Levenberg-Marquardt Algorithm classifies ECG beats better as compared to Back Propagation Neural Network (BPNN). FAR, FRR and accuracy parameters are used for detecting the ECG disease. The simulation process is undergone by using MATLAB simulation tool.