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

Loading...
Thumbnail Image
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2021
Major/Subject
Mcode
Degree programme
Language
en
Pages
11
83876-83886
Series
IEEE Access, Volume 9
Abstract
IoT 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 for300 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.
Description
Funding 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.
Keywords
Artificial intelligence, cervical cancer, feature selection, recurrence prediction, risk score, the Internet of Things (IoT)
Other note
Citation
Senthilkumar , 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 . https://doi.org/10.1109/ACCESS.2021.3087022