Improving the Accuracy for Analyzing Heart Diseases Prediction Based on the Ensemble Method

Xiao-Yan Gao, Abdelmegeid Amin Ali, Hassan Shaban Hassan, Eman M. Anwar and Ahmed Mostafa Khalil

Complexity, 2021, vol. 2021, 1-10

Abstract: 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.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/complexity/2021/6663455.pdf (application/pdf)
http://downloads.hindawi.com/journals/complexity/2021/6663455.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:6663455

DOI: 10.1155/2021/6663455

Access Statistics for this article

More articles in Complexity from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().