  Investigation on the electrocardiogram (ECG) signals is an essential way to
diagnose heart disease since the ECG process is noninvasive and easy to use.
This work presents a supraventricular arrhythmia prediction model consisting of
a few stages, including filtering of noise, a unique collection of ECG
characteristics, and automated learning classifying model to classify distinct
types, depending on their severity. We de-trend and de-noise a signal to reduce
noise to better determine functionality before extractions are performed. After
that, we present one R-peak detection method and Q-S detection method as a part
of necessary feature extraction. Next parameters are computed that correspond
to these features. Using these characteristics, we have developed a
classification model based on machine learning that can successfully categorize
different types of supraventricular tachycardia. Our findings suggest that
decision-tree-based models are the most efficient machine learning models for
supraventricular tachycardia arrhythmia. Among all the machine learning models,
this model most efficiently lowers the crucial signal misclassification of
supraventricular tachycardia. Experimental results indicate satisfactory
improvements and demonstrate a superior efficiency of the proposed approach
with 97% accuracy.
