A novel hybrid dimension reduction technique for efficient selection of bio-marker genes and prediction of heart failure status of patients

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dc.contributor.authorKazeem Adesina Dauda
dc.contributor.authorKabir Opeyemi Olorede
dc.contributor.authorSamuel Adewale Aderoju
dc.date.accessioned2023-07-27T08:22:41Z
dc.date.available2023-07-27T08:22:41Z
dc.date.issued2021-05-02
dc.description.abstractThis study highlighted and provided a conceptual framework of a hybridized dimension reduction by combining Genetic Algorithms (GA) and Boruta Algorithm (BA) with Deep Neural Network (DNN). Among questions left unanswered sufficiently by both computational and biological scientists are: which genes among thousand of genes are statistically relevant to the prediction of patients’ heart rhythm? and how they are associated with heart rhythm? A plethora of models has been proposed to reliably identify core informative genes. The premise of this present work is to address these limitations. Five distinct micro-array data on heart diseases have been taken into consideration to observe the prominent genes. We form three distinct set two-way hybrids between Boruta Algorithm and Neural Network (BANN); Genetic Algorithm and Deep Neural Network (GADNN) and Boruta Algorithm and Deep Neural Network (BADNN), respectively, to extract highly differentially expressed genes to achieve both better estimation and clearer interpretation of the parameters included in these models. The results of the filtering process were observed to be impressive since the technique removed noisy genes. The proposed BA algorithm was observed to select minimum genes in the wrapper process with about 80% of the five datasets than the proposed GA algorithm with 20%. Moreover, the empirical comparative results revealed that BADNN outperformed other proposed algorithms with prediction ac curacy of 97%, 87%, and 100% respectively. Finally, this study has successfully demonstrated the utility, versatility, and applicability of hybrid dimension reduction algorithms (HDRA) in the realm of deep neural networks.
dc.identifier.doi10.1016/j.sciaf.2021.e00778
dc.identifier.issn2468-2276
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/725
dc.language.isoen
dc.publisherScientific African
dc.relation.ispartofScientific African
dc.relation.ispartofseries12
dc.titleA novel hybrid dimension reduction technique for efficient selection of bio-marker genes and prediction of heart failure status of patients
dc.typejournal-article
oaire.citation.volume12
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