Advanced Clinical Diagnostic Models for ECG Pulse Classification in Arrhythmia Detection

The significance of observing abnormalities in the human heartbeat is in detecting the presence of arrhythmia in any given population. Arrhythmia is the condition wherein the heart beats at an irregular pacing, whether faster or slower. This occurrence can be associated with various cardiovascular risks posing threats to vital organs; hence immediate diagnosis is recommended. This paper aims to provide reliable insights of recognizing regular and irregular pulse readings through the utilization of three predictive algorithms. Through an ensemble approach, featuring a voting classification between the predictions of three learning models; logistic regression, decision tree classifier, and support vector classification, a diagnostic model was designed to improve clinical decisions. This approach was made to ensure a reliable decision support system through the consideration of several machine learning model predictions, eliminating risks of misclassification due to errors, biases, and irregularities.