Improving the Accuracy for Analyzing Heart Diseases Prediction Based on the Ensemble Method
Heart disease is the deadliest disease and one of leading causes of death worldwide. Machine learning is playing an essential role in the medical side. In this paper, ensemble learning methods are used to enhance the performance of predicting heart disease. Two features of extraction methods: linear...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6663455 |
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| _version_ | 1832550811268284416 |
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| author | Xiao-Yan Gao Abdelmegeid Amin Ali Hassan Shaban Hassan Eman M. Anwar |
| author_facet | Xiao-Yan Gao Abdelmegeid Amin Ali Hassan Shaban Hassan Eman M. Anwar |
| author_sort | Xiao-Yan Gao |
| collection | DOAJ |
| description | Heart disease is the deadliest disease and one of leading causes of death worldwide. Machine learning is playing an essential role in the medical side. In this paper, ensemble learning methods are used to enhance the performance of predicting heart disease. Two features of extraction methods: linear discriminant analysis (LDA) and principal component analysis (PCA), are used to select essential features from the dataset. The comparison between machine learning algorithms and ensemble learning methods is applied to selected features. The different methods are used to evaluate models: accuracy, recall, precision, F-measure, and ROC.The results show the bagging ensemble learning method with decision tree has achieved the best performance. |
| format | Article |
| id | doaj-art-07d0a0ff9ebc4a64b43ec3b1fb22e3e1 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-07d0a0ff9ebc4a64b43ec3b1fb22e3e12025-02-03T06:05:42ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66634556663455Improving the Accuracy for Analyzing Heart Diseases Prediction Based on the Ensemble MethodXiao-Yan Gao0Abdelmegeid Amin Ali1Hassan Shaban Hassan2Eman M. Anwar3School of Mathematics and Statistics, Yulin University, Yulin 719000, ChinaFaculty of Computers and Information, Department of Computer Science, Minia University, Minya, EgyptFaculty of Computers and Information, Department of Computer Science, Minia University, Minya, EgyptFaculty of Computers and Information, Department of Information System, Minia University, Minya, EgyptHeart disease is the deadliest disease and one of leading causes of death worldwide. Machine learning is playing an essential role in the medical side. In this paper, ensemble learning methods are used to enhance the performance of predicting heart disease. Two features of extraction methods: linear discriminant analysis (LDA) and principal component analysis (PCA), are used to select essential features from the dataset. The comparison between machine learning algorithms and ensemble learning methods is applied to selected features. The different methods are used to evaluate models: accuracy, recall, precision, F-measure, and ROC.The results show the bagging ensemble learning method with decision tree has achieved the best performance.http://dx.doi.org/10.1155/2021/6663455 |
| spellingShingle | Xiao-Yan Gao Abdelmegeid Amin Ali Hassan Shaban Hassan Eman M. Anwar Improving the Accuracy for Analyzing Heart Diseases Prediction Based on the Ensemble Method Complexity |
| title | Improving the Accuracy for Analyzing Heart Diseases Prediction Based on the Ensemble Method |
| title_full | Improving the Accuracy for Analyzing Heart Diseases Prediction Based on the Ensemble Method |
| title_fullStr | Improving the Accuracy for Analyzing Heart Diseases Prediction Based on the Ensemble Method |
| title_full_unstemmed | Improving the Accuracy for Analyzing Heart Diseases Prediction Based on the Ensemble Method |
| title_short | Improving the Accuracy for Analyzing Heart Diseases Prediction Based on the Ensemble Method |
| title_sort | improving the accuracy for analyzing heart diseases prediction based on the ensemble method |
| url | http://dx.doi.org/10.1155/2021/6663455 |
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