Forecasting and Categorization of Cardiac Arrhythmia using Soft Computational Approaches

Computer Science and Engineering

Health is extremely crucial in the existence of humans, and each part of the human body plays a substantial function in maintaining a well-being lifestyle. Arrhythmia illness is a severe issue in the heart. This disease must be recognized early and preventative measures implemented. Despite remarkable advancements in the medical field, heart arrhythmia remains a difficulty for medical practitioners. As a result, it must be carefully classified in order to diagnose the signs of this condition, which may assist the medical practitioner in treating the patient in an avoidable manner. Despite numerous studies, there are not enough methods available to classify arrhythmias accurately. As a result, this study developed a data mining approach known as K-Nearest Neighbour (KNN) to classify cardiac arrhythmia disorder. This proposed approach yields a beneficial result of 98.1% accuracy and 93.2% precision

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