Browsing by Author "Nnodim, C.T."

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    An Adaptive Genetic Algorithm with Recursive Feature Elimination Approach for Predicting Malaria Vector Gene Expression Data Classification Using Support Vector Machine
    (Walailak University - Walailak Journal of Science and Technology, 2021) Arowolo, M.O.; Adebiyi, M.O.; Nnodim, C.T.; Abdulsalam, S.O.; Adebiyi, A.A.
    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. Keywords: RNA-seq, Adaptive genetic algorithm, Recursive feature elimination, Malaria vector, Support Vector Machine kernels
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    Performance Evaluation of ANOVA and RFE Algorithms for Classifying Microarray Dataset Using SVM
    (Springer Nature Switzerland, Lecture Notes in Business Information Processing 402: 480 – 492 - Proceedings of 17th European, Mediterranean, and Middle Eastern Conference, EMCIS 2020, Dubai, United Arab Emirates, November 25–26, 2020, 2020) Abdulsalam, S.O.; Abubakar, A.M.; Ajao, J.F.; Babatunde, R.S.; Ogundokun, R.O.; Nnodim, C.T.; Arowolo, M.O.
    A significant application of microarray gene expression data is the classification and prediction of biological models. An essential component of data analysis is dimension reduction. This study presents a comparison study on a reduced data using Analysis of Variance (ANOVA) and Recursive Feature Elimination (RFE) feature selection dimension reduction techniques, and evaluates the relative performance evaluation of classification procedures of Support Vector Machine (SVM) classification technique. In this study, an accuracy and computational performance metrics of the processes were carried out on a microarray colon cancer dataset for classification, SVM-RFE achieved 93% compared to ANOVA with 87% accuracy in the classification output result. Keywords: SVM-RFE; ANOVA; Microarray; SVM; Cancer