Design of an Explainable AI-Assisted Arrhythmia Detection and Interpretation Model Using ECG Data

Arrhythmia is a common cardiac condition characterized by irregular heartbeats that, if left untreated, can have major health effects. In this paper, we present a random forest-based binary classification method for identifying arrhythmia. The random forest approach is an ensemble learning methodology that can handle large datasets with little overfitting, making it a good choice for analysing medical data. Moreover, the interpretability of the model is further improved by the Explainable AI in the form of decision tree analysis, which offers vital insights into the individual ECG aspects impacting the classification choices. The science of arrhythmia diagnosis and patient treatment is finally advanced because of this openness, which also helps physicians make wise decisions based on the model’s predictions. The experiment was performed on the MIT-BIH ECG dataset by applying a 75–25 split. The accuracy of 98.99 was achieved using random forest classifier, and then using decision tree explainer, we found out the most significant features for the classification, and the explanation tree illustration helps decode the reason for the classification of the data for normal or arrhythmia classes. This explainer model will help the healthcare personnel to better understand and take decision for the betterment of the patients.