A Implementation of the K-Nearest Neighbors (KNN) Method for Classification of Heart Disease
DOI:
https://doi.org/10.33379/gtech.v8i3.4495Keywords:
K-Nearest Neighbors (KNN), Classification, Heart Disease, RapidminerAbstract
Heart disease is one of the leading causes of death globally. This disease is also called coronary heart disease. This heart disease occurs when blood entering the heart muscle is stopped/blocked, the result of this condition is that it causes serious damage to the heart. Even though this disease is not contagious, data from the World Health Organization (WHO) states that this cardiovascular disease claims around 17.9 million lives every year. The dataset used in this research consists of 303 data and consists of 14 attributes that can be used to predict the possibility of heart disease. The application used is Rapidminer version 9.10 and the method used is the K-Nearest Neighbors (KNN) method, this method is a method for classifying objects based on learning data that is closest to the object. The results of the KNN method with parameter K=5 are that the accuracy value obtained is 64.03%, the precession value is 64.58%, and the recall value obtained is 75.15%.
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