Applying k-Nearest Neighbour in Diagnosing Heart Disease Patients

Abstract

Heart disease is the leading cause of death in the world over the past 10 years. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease. K-Nearest-Neighbour(KNN) is one of the successful data mining techniques used in classification problems. However, it is less used in the diagnosis of heart disease patients. Recently, researchers are showing that combining different classifiers through voting is outperforming other single classifiers. This paper investigates applying KNN to help healthcare professionals in the diagnosis of heart disease. It also investigates if integrating voting with KNN can enhance its accuracy in the diagnosis of heart disease patients. The results show that applying KNN could achieve higher accuracy than neural network ensemble in the diagnosis of heart disease patients. The results also show that applying voting could not enhance the KNN accuracy in the diagnosis of heart disease.