Cardiac Abnormality Detection Using Adaptive Neuro-Fuzzy Inference System - Naibaho, Ono Iyan (JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP), 2025)
Heart defects are one of the leading causes of death worldwide, making early detection crucial to prevent more serious complications. Electrocardiogram signals are an important diagnostic tool that can be used to detect heart abnormalities in real-time. In this study, an Adaptive Neuro-Fuzzy Inference System artificial intelligence model is used to analyze ECG signal data and detect heart abnormalities early. The ECG signal data used was taken from 30 research subjects, then processed to reduce distracting noise. The combination of artificial neural networks and fuzzy systems aims to overcome the problem of uncertainty in ECG signal data. Thus, this method can be used as a solution that helps in the early diagnosis of heart disorders. The performance evaluation of the proposed Adaptive Neuro-Fuzzy Inference System revealed a perfect True Positive Rate of 1.0 on the Receiver Operating Characteristic (ROC) curve, demonstrating its exceptional ability to correctly identify all instances of cardiac abnormality within the dataset.