  Intricating cardiac complexities are the primary factor associated with
healthcare costs and the highest cause of death rate in the world. However,
preventive measures like the early detection of cardiac anomalies can prevent
severe cardiovascular arrests of varying complexities and can impose a
substantial impact on healthcare cost. Encountering such scenarios usually the
electrocardiogram (ECG or EKG) is the first diagnostic choice of a medical
practitioner or clinical staff to measure the electrical and muscular fitness
of an individual heart. This paper presents a system which is capable of
reading the recorded ECG and predict the cardiac anomalies without the
intervention of a human expert. The paper purpose an algorithm which read and
perform analysis on electrocardiogram datasets. The proposed architecture uses
the Discrete Wavelet Transform (DWT) at first place to perform preprocessing of
ECG data followed by undecimated Wavelet transform (UWT) to extract nine
relevant features which are of high interest to a cardiologist. The
probabilistic mode named Bayesian Network Classifier is trained using the
extracted nine parameters on UCL arrhythmia dataset. The proposed system
classifies a recorded heartbeat into four classes using Bayesian Network
classifier and Tukey's box analysis. The four classes for the prediction of a
heartbeat are (a) Normal Beat, (b) Premature Ventricular Contraction (PVC) (c)
Premature Atrial Contraction (PAC) and (d) Myocardial Infarction. The results
of experimental setup depict that the proposed system has achieved an average
accuracy of 96.6 for PAC\% 92.8\% for MI and 87\% for PVC, with an average
error rate of 3.3\% for PAC, 6\% for MI and 12.5\% for PVC on real
electrocardiogram datasets including Physionet and European ST-T Database
(EDB).
