K-Nearest Neighbor Algorithm for Efficient Heart Disease Classification System

Healthcare is considered significant topic in the recent research area. However, one of the most commonly diseases which is heart diseases disease. The possibility of early detection to reduce the number of deaths because it is difficult to predict a heart disorder quickly. Recently, many researchers focused on the implementation of several feature extraction techniques and the help of artificial intelligence algorithms to classify this disease, but classification accuracy remained the only difference between these studies. In this paper, the proposed work for the classification of heart diseases was implemented and tested after selecting methods and techniques for data pre-processing and extracting important features that led to obtaining a competitive classification accuracy that reached higher than 93.5% compared to related studies. This finding encourages us and other field researchers to use methods for feature extraction and other strategies described in this paper to classify other diseases.