Performance Evaluation of Data Mining Classification Techniques for Heart Disease Prediction
Journal Title: American journal of Engineering Research - Year 2018, Vol 7, Issue 2
Abstract
Heart disease might be one of the foremost causes to death. Because of the lack of skilled knowledge or experiences of real-life practitioners about heart failure symptoms for an early prediction, it is not an easy task to detect the disease. Consequently, computer-based prediction of heart disease may play a significant role as a pre-stage detection to take proper actions with a view to recovering from it. However, the choice of the proper data mining classification method can effectively predict the early stage of the disease for being recurred from it. In this paper, the three mostly used classification techniques such as support vector machine (SVM), k-nearest neighbor (KNN) and artificial neural network (ANN) have been studied with a view to evaluating them for heart disease prediction using Cleveland standard heart disease dataset. The experimental result shows that the classification accuracy using SVM (85.1852%) outperforms that of using KNN (82.963%) and ANN (73.3333%).
Authors and Affiliations
Md. Fazle Rabbi, Md. Palash Uddin, Md. Arshad Ali, Md. Faruk Kibria, Masud Ibn Afjal, Md. Safiqul Islam, Adiba Mahjabin Nitu
Keywords
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