Performances analysis of heart disease dataset using different data mining classifications
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 f...
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| Format: | Article |
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Insight - Indonesian Society for Knowledge and Human Development
2018
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| _version_ | 1796869296452796416 |
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| author | Wan Zunaidi, Wan Hajarul Asikin Saedudin, RD Rohmat Ali Shah, Zuraini Kasim, Shahreen Sen, Seah Choon Abdurohman, Maman |
| author_facet | Wan Zunaidi, Wan Hajarul Asikin Saedudin, RD Rohmat Ali Shah, Zuraini Kasim, Shahreen Sen, Seah Choon Abdurohman, Maman |
| author_sort | Wan Zunaidi, Wan Hajarul Asikin |
| collection | UTHM |
| description | 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. |
| first_indexed | 2024-03-05T21:52:45Z |
| format | Article |
| id | uthm.eprints-6041 |
| institution | Universiti Tun Hussein Onn Malaysia |
| last_indexed | 2024-03-05T21:52:45Z |
| publishDate | 2018 |
| publisher | Insight - Indonesian Society for Knowledge and Human Development |
| record_format | dspace |
| spelling | uthm.eprints-60412022-01-26T03:01:37Z http://eprints.uthm.edu.my/6041/ Performances analysis of heart disease dataset using different data mining classifications Wan Zunaidi, Wan Hajarul Asikin Saedudin, RD Rohmat Ali Shah, Zuraini Kasim, Shahreen Sen, Seah Choon Abdurohman, Maman TA168 Systems engineering 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. Insight - Indonesian Society for Knowledge and Human Development 2018 Article PeerReviewed Wan Zunaidi, Wan Hajarul Asikin and Saedudin, RD Rohmat and Ali Shah, Zuraini and Kasim, Shahreen and Sen, Seah Choon and Abdurohman, Maman (2018) Performances analysis of heart disease dataset using different data mining classifications. International Journal on Advanced Science Engineering Information Technology, 8 (6). pp. 2677-2682. ISSN 2088-5334 |
| spellingShingle | TA168 Systems engineering Wan Zunaidi, Wan Hajarul Asikin Saedudin, RD Rohmat Ali Shah, Zuraini Kasim, Shahreen Sen, Seah Choon Abdurohman, Maman Performances analysis of heart disease dataset using different data mining classifications |
| title | Performances analysis of heart disease dataset using different data mining classifications |
| title_full | Performances analysis of heart disease dataset using different data mining classifications |
| title_fullStr | Performances analysis of heart disease dataset using different data mining classifications |
| title_full_unstemmed | Performances analysis of heart disease dataset using different data mining classifications |
| title_short | Performances analysis of heart disease dataset using different data mining classifications |
| title_sort | performances analysis of heart disease dataset using different data mining classifications |
| topic | TA168 Systems engineering |
| work_keys_str_mv | AT wanzunaidiwanhajarulasikin performancesanalysisofheartdiseasedatasetusingdifferentdataminingclassifications AT saedudinrdrohmat performancesanalysisofheartdiseasedatasetusingdifferentdataminingclassifications AT alishahzuraini performancesanalysisofheartdiseasedatasetusingdifferentdataminingclassifications AT kasimshahreen performancesanalysisofheartdiseasedatasetusingdifferentdataminingclassifications AT senseahchoon performancesanalysisofheartdiseasedatasetusingdifferentdataminingclassifications AT abdurohmanmaman performancesanalysisofheartdiseasedatasetusingdifferentdataminingclassifications |