DOI : https://doi.org/10.18517/ijaseit.8.6.5042

Performances Analysis of Heart Disease Dataset using Different Data Mining Classifications

Wan Hajarul Asikin Wan Zunaidi (1), RD Rohmat Saedudin (2), Zuraini Ali Shah (3), Shahreen Kasim (4), Choon Sen Seah (5), Maman Abdurohman (6)
(1) Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia
(2) School of Industrial Engineering, Telkom University, 40257 Bandung, West Java, Indonesia
(3) Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia
(4) Universiti Tun Hussein Onn Malaysia
(5) 3Faculty of Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia
(6) School of Computing, Telkom University, 40257 Bandung, West Java, Indonesia
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How to cite (IJASEIT) :
Wan Zunaidi, Wan Hajarul Asikin, et al. “Performances Analysis of Heart Disease Dataset Using Different Data Mining Classifications”. International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 6, Dec. 2018, pp. 2677-82, doi:10.18517/ijaseit.8.6.5042.
Citation Format :
nowadays, heart disease is one of the major diseases that cause death. It is a 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 and prediction of heart disease. This research studied the prediction of heart disease by using five different techniques in WEKA tools by using the input attributes of the dataset. This research used 13 attributes, such as sex, blood pressure, cholesterol and other medical terms to detect the likelihood of a patient getting heart disease. The classification techniques, namely J48, Decision Stump, Random Forest, Sequential Minimal Optimization (SMO), and Multilayer Perceptron used to analyze the heart disease. Performance measurement for this study are the accuracy of correct classification, mean absolute error and kappa statistics of the classifier. The result shows that Multilayer Perceptron Neural Networks is the most suited for early prediction of heart diseases.

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