EXPLAINABLE ARTIFICIAL INTELLIGENCE THEORY IN DECISION MAKING TREATMENT OF ARITHMIA PATIENTS WITH USING DEEP LEARNING MODELS

Abstract

In the context of Explainable Artificial Intelligence, there are two important keywords: interpretability and "explainability". Interpretability is the extent to which humans can understand the causes of decisions. The better the interpretability of an AI/ML model, the easier it is for someone to understand why certain decisions or predictions have been made. Some cases of AI/ML implementation may not require explanation, because they are used in a low-risk environment, meaning mistakes will not have serious consequences. The need for interpretability and explainability arises when an AI system is used for certain high-risk problems or tasks, so it is not enough just to get predictive/classification decision outputs, but also needs explanations to convince users that AI (1: Model Explainability) is working the right way and (2: Decision Explainability) has made the right decision (Hotma, 2022). This research provides benefits for the development of knowledge regarding the implementation model of Explainable AI Theory in assisting Doctors' Decision Making for patients with cardiac arrhythmias with the Deep Learning Model in assisting Doctors' Decision Making for patients with cardiac arrhythmias. Knowing the Deep Learning Algorithm can be used in Machine Learning to read EKG Results. Knowing how to improve the results of the accuracy of the Explainable AI Application in Decision Making by Doctors for patients with cardiac arrhythmias. The use of Explanable Artificial Intelligence in the management of arrhythmia patients can provide an interpretation for doctors to be more optimal in treating patients. The results of this AI machine decision can increase doctors' confidence in treating arrhythmia patients optimally, effectively and efficiently. And also treatment will be faster because it is assisted by tools, so that patients can be treated more quickly. Thus it will reduce the mortality rate in arrhythmia patients.