TY - JOUR
T1 - Incorporating Artificial Fish Swarm in Ensemble Classification Framework for Recurrence Prediction of Cervical Cancer
AU - Senthilkumar, Geeitha
AU - Ramakrishnan, Jothilakshmi
AU - Frnda, Jaroslav
AU - Ramachandran, Manikandan
AU - Gupta, Deepak
AU - Tiwari, Prayag
AU - Shorfuzzaman, Mohammad
AU - Mohammed, Mazin Abed
N1 - 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.
PY - 2021
Y1 - 2021
N2 - 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 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.
AB - 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 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.
KW - Artificial intelligence
KW - cervical cancer
KW - feature selection
KW - recurrence prediction
KW - risk score
KW - the Internet of Things (IoT)
UR - http://www.scopus.com/inward/record.url?scp=85111016568&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2021.3087022
DO - 10.1109/ACCESS.2021.3087022
M3 - Article
AN - SCOPUS:85111016568
SN - 2169-3536
VL - 9
SP - 83876
EP - 83886
JO - IEEE Access
JF - IEEE Access
M1 - 9447707
ER -
