  In primary diagnosis and analysis of heart defects, an ECG signal plays a
significant role. This paper presents a model for the prediction of ventricular
tachycardia arrhythmia using noise filtering, a unique set of ECG features, and
a machine learning-based classifier model. Before signal feature extraction, we
detrend and denoise the signal to eliminate the noise for detecting features
properly. After that necessary features have been extracted and necessary
parameters related to these features are measured. Using these parameters, we
prepared one efficient multiclass classifier model using a machine learning
approach that can classify different types of ventricular tachycardia
arrhythmias efficiently. Our results indicate that Logistic regression and
Decision tree-based models are the most efficient machine learning models for
detecting ventricular tachycardia arrhythmia. In order to diagnose heart
diseases and find care for a patient, an early, reliable diagnosis of different
types of arrhythmia is necessary. By implementing our proposed method, this
work deals with the problem of reducing the misclassification of the critical
signal related to ventricular tachycardia very efficiently. Experimental
findings demonstrate satisfactory enhancements and demonstrate high resilience
to the algorithm that we have proposed. With this assistance, doctors can
assess this type of arrhythmia of a patient early and take the right decision
at the proper time.
