An efficient recurrent neural network with ensemble classifier-based weighted model for disease prediction
Abstract: Day-to-day lives are affected globally by the epidemic coronavirus 2019. With an increasing number of positive cases, India has now become a highly affected country. Chronic diseases affect individuals with no time identification and impose a huge disease burden on society. In this article, an Efficient Recurrent Neural Network with Ensemble Classifier (ERNN-EC) is built using VGG-16 and Alexnet with weighted model to predict disease and its level. The dataset is partitioned randomly into small subsets by utilizing mean-based splitting method. Various models of classifier create a homogeneous ensemble by utilizing an accuracy-based weighted aging classifier ensemble, which is a weighted model’s modification. Two state of art methods such as Graph Sequence Recurrent Neural Network and Hybrid Rough-Block-Based Neural Network are used for comparison with respect to some parameters such as accuracy, precision, recall, f1-score, and relative absolute error (RAE). As a result, it is found that the proposed ERNN-EC method accomplishes accuracy of 95.2%, precision of 91%, recall of 85%, F1-score of 83.4%, and RAE of 41.6%.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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An efficient recurrent neural network with ensemble classifier-based weighted model for disease prediction ; volume:31 ; number:1 ; year:2022 ; pages:979-991 ; extent:13
Journal of intelligent systems ; 31, Heft 1 (2022), 979-991 (gesamt 13)
- Creator
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Kesavan, Tamilselvi
Krishnamoorthy, Ramesh Kumar
- DOI
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10.1515/jisys-2022-0068
- URN
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urn:nbn:de:101:1-2022082614045895566913
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:24 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Kesavan, Tamilselvi
- Krishnamoorthy, Ramesh Kumar
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