APPLICATION OF KNN METHOD FOR CLASSIFICATION OF ARRHYTHMIA TYPES BASED ON ECG DATA
DOI:
https://doi.org/10.47709/cnahpc.v7i3.6010Dimension:
Keywords:
Arrhythmia, KNN, ECG, ClassificationAbstract
World Health Organization (WHO) data from June 2024 shows that 31% of adults worldwide or 1.8 billion people do not do physical activity. With that, adults are at higher risk of developing cardiovascular disease and causing an economic and social burden on people with heart disease. K-Nearest Neighbor (KNN) is a machine learning method that can be used to classify or predict heart disease conditions. KNN works by finding the closest data point in the training dataset and then using the class labels of those neighbors to classify new data points. In the context of heart disease, this can be used to predict the likelihood of someone having heart disease. Recording the electrical activity of the heart using a 3-led ECG to determine heart health as well as being material for classification. Exploring the use in the diagnosis of heart disease by focusing on screening and classification of heart disease. By utilizing the KNN method, it has the potential to produce a model that can assist in clinical decision making. Improving the prevention of heart disease and accelerating diagnosis through more sophisticated and technology-based analysis of patient health data.
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Copyright (c) 2025 Sonatafati Manao, Delima Sitanggang, Albert Sagala, Ade Oktarino, Mardi Turnip
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