International Journal on Advanced Science, Engineering and Information Technology, Vol. 8 (2018) No. 6, DOI:10.18517/ijaseit.8.6.5042

Performances Analysis of Heart Disease Dataset using Different Data Mining Classifications

Shahreen Kasim, Wan Hajarul Asikin Wan Zunaidi, Zuraini Ali Shah, Rodziah Atan


nowadays, heart disease is one of the major diseases that cause death. It is matter for us to concern in today’s highly chaotic life style that leads to various diseases. Early prediction of identification to heart related diseases has been investigated by many researchers. The death rate can be further brought down if we can predict or identify the heart disease earlier. There are many studies that explore the different classification algorithms for classification or prediction of heart disease. This paper has study the predicting heart disease by using five different techniques in WEKA tools for heart disease using more number of input attributes. This research uses medical terms such as sex, blood pressure, cholesterol like 13 attributes to detect the likelihood of patient getting a heart disease. The classification techniques, namely J48, Decision Stump, Random Forest, Sequential Minimal Optimization (SMO), and Multilayer Perceptron used to analyse the heart disease. Performance of these techniques is compared based on accuracy of correctly classification, mean absolute error and kappa statistics of classifier. This analysis shows that out of these five classification techniques, Neural Networks Multilayer Perceptron is the most suited for early prediction of heart diseases.


WEKA; data mining; attribute selection; classification; heart disease

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