Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/15630
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dc.contributor.authorSravanthi, Jakkula-
dc.contributor.authorReddy, Chada Sampath-
dc.contributor.authorMahendar, A-
dc.contributor.authorKumar, V Ravi-
dc.contributor.authorBuragadda, Swathi-
dc.contributor.authorGhantasala, G S Pradeep-
dc.contributor.authorGupta, Gaurav-
dc.date.accessioned2024-05-29T08:51:25Z-
dc.date.available2024-05-29T08:51:25Z-
dc.date.issued2024-
dc.identifier.citationpp. 1792-1797en_US
dc.identifier.isbn9789380544519-
dc.identifier.urihttp://dx.doi.org/10.23919/INDIACom61295.2024.10498390-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/15630-
dc.description.abstractThis paper discusses the significance of Machine Learning (ML) and Deep Learning (DL) techniques for structured and unstructured healthcare data. As healthcare data is increasing tremendously, it is difficult to identify hidden patterns in huge amounts of data. DL handles a massive amount of clinical data and provides better outcomes. A novel competitive ensemble deep learning model has been proposed to improve the classification performance of structured data. However, dealing with unstructured data, the proposed work highlights a competitive DL model for Twitter sentiment analysis. In addition, this paper discusses the proposed Competitive Ensemble Deep Learning (CEPL) algorithm for text data. The proposed model is compared with a traditional model to evaluate the model's performance in the range of 0.2%-0.5%. © 2024 Bharati Vidyapeeth, New Delhi.en_US
dc.language.isoenen_US
dc.publisher11th International Conference on Computing for Sustainable Global Development, INDIACom 2024en_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.subjectAccuracyen_US
dc.subjectCeplen_US
dc.subjectDeep Learningen_US
dc.subjectHealthcare Dataen_US
dc.subjectMachine Learning Methodsen_US
dc.titleImprove Accuracy in Healthcare Data Analysis Using Competitive Ensemble Deep Learning Modelen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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