An Adaptive Genetic Algorithm with Recursive Feature Elimination Approach for Predicting Malaria Vector Gene Expression Data Classification using Support Vector Machine Kernels

Micheal Olaolu  AROWOLO; Marion Olubunmi  ADEBIYI; Chiebuka Timothy  NNODIM; Sulaiman Olaniyi  ABDULSALAM; Ayodele Ariyo  ADEBIYI

doi:10.48048/wjst.2021.9849

Authors

Micheal Olaolu AROWOLO Department of Computer Science, Landmark University, Omu-Aran, Kwara State, Nigeria https://orcid.org/0000-0002-9418-5346
Marion Olubunmi ADEBIYI Department of Computer Science, Landmark University, Omu-Aran, Kwara State, Nigeria
Chiebuka Timothy NNODIM Department Department of Mechanical Engineering, Landmark University, Omu-Aran, Kwara State, Nigeria
Sulaiman Olaniyi ABDULSALAM Department of Computer Science, Kwara State University, Malete, Nigeria
Ayodele Ariyo ADEBIYI Department of Computer Science, Landmark University, Omu-Aran, Kwara State, Nigeria

DOI:

https://doi.org/10.48048/wjst.2021.9849

Keywords:

RNA-seq, Adaptive genetic algorithm, Recursive feature elimination, Malaria vector, Support Vector Machine kernels

Abstract

As mosquito parasites breed across many parts of the sub-Saharan Africa part of the world, infected cells embrace an unpredictable and erratic life period. Millions of individual parasites have gene expressions. Ribonucleic acid sequencing (RNA-seq) is a popular transcriptional technique that has improved the detection of major genetic probes. The RNA-seq analysis generally requires computational improvements of machine learning techniques since it computes interpretations of gene expressions. For this study, an adaptive genetic algorithm (A-GA) with recursive feature elimination (RFE) (A-GA-RFE) feature selection algorithms was utilized to detect important information from a high-dimensional gene expression malaria vector RNA-seq dataset. Support Vector Machine (SVM) kernels were used as the classification algorithms to evaluate its predictive performances. The feasibility of this study was confirmed by using an RNA-seq dataset from the mosquito Anopheles gambiae. The technique results in related performance had 98.3 and 96.7 % accuracy rates, respectively.

HIGHLIGHTS

Dimensionality reduction method based of feature selection
Classification using Support vector machine
Classification of malaria vector dataset using an adaptive GA-RFE-SVM

GRAPHICAL ABSTRACT

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