Incorporating Artificial Fish Swarm in Ensemble Classification Framework for Recurrence Prediction of Cervical Cancer

dc.contributorAalto Universityen
dc.contributor.authorSenthilkumar, Geeithaen_US
dc.contributor.authorRamakrishnan, Jothilakshmien_US
dc.contributor.authorFrnda, Jaroslaven_US
dc.contributor.authorRamachandran, Manikandanen_US
dc.contributor.authorGupta, Deepaken_US
dc.contributor.authorTiwari, Prayagen_US
dc.contributor.authorShorfuzzaman, Mohammaden_US
dc.contributor.authorMohammed, Mazin Abeden_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.contributor.organizationAnna Universityen_US
dc.contributor.organizationMazharul Uloom Collegeen_US
dc.contributor.organizationUniversity of Zilinaen_US
dc.contributor.organizationGuru Gobind Singh Indraprastha Universityen_US
dc.contributor.organizationTaif Universityen_US
dc.contributor.organizationUniversity of Anbaren_US
dc.descriptionFunding Information: This work was supported in part by the Grant System of University of Zilina No. 1/2020 under Project 7962, in part by the Slovak Grant Agency for Science (VEGA) under Grant 1/0157/21, and in part by the Taif University Researchers, Taif University, Taif, Saudi Arabia, under Grant TURSP-2020/79. Publisher Copyright: © 2013 IEEE.
dc.description.abstractIoT has facilitated predominant advancements in cancer research by incorporating Artificial intelligence (AI) that enables human decision-makers to achieve a better decision. Recently, Least Absolute Shrinkage and Selection Operator (LASSO) classifier has launched in predicting recurrence cancer genes in the cervix. At the initial phase, the recurrence gene expression of lncRNA is collected from Geo Datasets. Secondly, data imputation, accomplished with Mode and Mean Missing method (MMM-DI). Thirdly, feature selection is compassed using Hilbert-Schmidt independence criterion with Diversity based Artificial Fish Swarm (HSDAFS). In the HSDA.FS algorithm, the diversity parameter is added based on the gene value, and their risk score of the lncRNAs is computed using the Artificial intelligence (AI) technique. Finally, recurrence prediction, an ENSemble Classification Framework (ENSCF), is proposed based on recurrent neural networks. The prognostic factor is computed with a risk score of nine lncRNA signatures for 300 samples taken from GSE44001. The Chi-Square method has been used to obtain statistical results. The survival of the patient with recurrence cervical cancer is shown using the proposed model.en
dc.description.versionPeer revieweden
dc.identifier.citationSenthilkumar, G, Ramakrishnan, J, Frnda, J, Ramachandran, M, Gupta, D, Tiwari, P, Shorfuzzaman, M & Mohammed, M A 2021, ' Incorporating Artificial Fish Swarm in Ensemble Classification Framework for Recurrence Prediction of Cervical Cancer ', IEEE Access, vol. 9, 9447707, pp. 83876-83886 .
dc.identifier.otherPURE UUID: 931d7c80-a98e-42ff-b771-8f108224ae35en_US
dc.identifier.otherPURE ITEMURL:
dc.identifier.otherPURE LINK:
dc.identifier.otherPURE FILEURL:
dc.relation.ispartofseriesIEEE Accessen
dc.relation.ispartofseriesVolume 9en
dc.subject.keywordArtificial intelligenceen_US
dc.subject.keywordcervical canceren_US
dc.subject.keywordfeature selectionen_US
dc.subject.keywordrecurrence predictionen_US
dc.subject.keywordrisk scoreen_US
dc.subject.keywordthe Internet of Things (IoT)en_US
dc.titleIncorporating Artificial Fish Swarm in Ensemble Classification Framework for Recurrence Prediction of Cervical Canceren
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi