Optimized Gene Classification using Support Vector Machine with Convolutional Neural Network for Cancer Detection from Gene Expression Microarray Data


Authors : Vishwas Victor; Dr. Ragini Shukla

Volume/Issue : Volume 8 - 2023, Issue 12 - December

Google Scholar : http://tinyurl.com/8kb9bmhw

Scribd : http://tinyurl.com/4zc2s2e9

DOI : https://doi.org/10.5281/zenodo.10438850

Abstract : There are numerous approaches for handling microarray gene expression data since new feature selection techniques are constantly being developed. To create a new subset of pertinent features, feature selection (FS) is utilized to pinpoint the essential feature subset. The model that used the informative subset projected that a classification model generated solely using this subset would have higher predicted accuracy than a model developed using the whole collection of attributes. We offer an analytical approach for cancer classification and developed a model using Support Vector Machine as classifier and after that Convolutional Neural Network in the aspect of Deep Learning. The outcome received in the context of the proposed model is very impressive and accurate.

Keywords : Feature selection; Optimization;Classification; Support Vector Machine (SVM); Deep Learning; Machine Learning; Convolutional Neural Network (CNN).

There are numerous approaches for handling microarray gene expression data since new feature selection techniques are constantly being developed. To create a new subset of pertinent features, feature selection (FS) is utilized to pinpoint the essential feature subset. The model that used the informative subset projected that a classification model generated solely using this subset would have higher predicted accuracy than a model developed using the whole collection of attributes. We offer an analytical approach for cancer classification and developed a model using Support Vector Machine as classifier and after that Convolutional Neural Network in the aspect of Deep Learning. The outcome received in the context of the proposed model is very impressive and accurate.

Keywords : Feature selection; Optimization;Classification; Support Vector Machine (SVM); Deep Learning; Machine Learning; Convolutional Neural Network (CNN).

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