An efficient recurrent neural network with ensemble classifier-based weighted model for disease prediction
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 Effic...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2022-08-01
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| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2022-0068 |
| _version_ | 1797998384528752640 |
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| author | Kesavan Tamilselvi Krishnamoorthy Ramesh Kumar |
| author_facet | Kesavan Tamilselvi Krishnamoorthy Ramesh Kumar |
| author_sort | Kesavan Tamilselvi |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-04-11T10:47:56Z |
| format | Article |
| id | doaj.art-ad3702b0cfc04df4afac57f9771d9548 |
| institution | Directory Open Access Journal |
| issn | 2191-026X |
| language | English |
| last_indexed | 2024-04-11T10:47:56Z |
| publishDate | 2022-08-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj.art-ad3702b0cfc04df4afac57f9771d95482022-12-22T04:29:00ZengDe GruyterJournal of Intelligent Systems2191-026X2022-08-0131197999110.1515/jisys-2022-0068An efficient recurrent neural network with ensemble classifier-based weighted model for disease predictionKesavan Tamilselvi0Krishnamoorthy Ramesh Kumar1Research and Development, Bharathiar University, Coimbatore, Tamil Nadu, IndiaDepartment of Computer Science, Bharathiar University, Coimbatore, Tamil Nadu, IndiaDay-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%.https://doi.org/10.1515/jisys-2022-0068disease predictionneural networkensemble classifiercovid-19preprocessingweighted model |
| spellingShingle | Kesavan Tamilselvi Krishnamoorthy Ramesh Kumar An efficient recurrent neural network with ensemble classifier-based weighted model for disease prediction Journal of Intelligent Systems disease prediction neural network ensemble classifier covid-19 preprocessing weighted model |
| title | An efficient recurrent neural network with ensemble classifier-based weighted model for disease prediction |
| title_full | An efficient recurrent neural network with ensemble classifier-based weighted model for disease prediction |
| title_fullStr | An efficient recurrent neural network with ensemble classifier-based weighted model for disease prediction |
| title_full_unstemmed | An efficient recurrent neural network with ensemble classifier-based weighted model for disease prediction |
| title_short | An efficient recurrent neural network with ensemble classifier-based weighted model for disease prediction |
| title_sort | efficient recurrent neural network with ensemble classifier based weighted model for disease prediction |
| topic | disease prediction neural network ensemble classifier covid-19 preprocessing weighted model |
| url | https://doi.org/10.1515/jisys-2022-0068 |
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