Arrhythmia Disease Detection Through ECG Signal Monitoring with Decision Tree Method

Arrhythmia is a heart disorder that is a leading cause of death worldwide. Early detection and proper management are essential to reduce its impact. With Machine Learning, cardiovascular disease detection can be performed more quickly, accurately, and efficiently. This study aims to classify patient data with arrhythmia using Machine Learning algorithms, specifically the Decision Tree method. The recorded electrocardiography (ECG) signals were processed through filtering and feature extraction to produce data that is easy to classify. The results showed that data processing using the Decision Tree method achieved an accuracy rate of 92.96%.