Enhancement of Breast Cancer Classification Using Bat Feature Selection with Recurrent Deep Learning

DNA is a valuable tool for classifying expression of genes in detection of breast cancer. Gene expression data are biological data that extract valuable hidden information from gene datasets. Extracting useful features from datasets is a challenging task. Our gene expression dataset had a small numb...

Full description

Saved in:
Bibliographic Details
Published in:Journal of computing and information technology Vol. 32; no. 3; pp. 195 - 215
Main Author: Jaafar, Ali Nafaa
Format: Journal Article Paper
Language:English
Published: Sveuciliste U Zagrebu 01.09.2024
Sveučilište u Zagrebu Fakultet elektrotehnike i računarstva
University of Zagreb Faculty of Electrical Engineering and Computing
Subjects:
Algorithms
bat algorithm
Bats
Breast cancer
Cancer
Comparative analysis
Diagnosis
feature selection
Gene expression
Genes
Genetic aspects
Genomics
GRU
LSTM
Machine learning
Neural networks
Oncology, Experimental
RNN
Iraq
ISSN:1330-1136, 1846-3908
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:DNA is a valuable tool for classifying expression of genes in detection of breast cancer. Gene expression data are biological data that extract valuable hidden information from gene datasets. Extracting useful features from datasets is a challenging task. Our gene expression dataset had a small number of samples but many features. This paper compared three types of recurrent deep learning models, including recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), for classification of breast cancer. The goals of the study were to improve the accuracy of classification and to enhance the effectiveness of feature selection; the basic principle was to select the best features from the original datasets. The bat algorithm assists in selecting the best relevant feature when integrated with recurrent deep learning models, which improves breast cancer classification by leveraging training datasets. Data preprocessing involves removing unnecessary columns and filling out missing values with the median value. The result was a comparative study using recurrent deep learning with the bat algorithm to classify breast cancer. The bat algorithm with LSTM achieved higher accuracy than RNN and GRU, where GRU had the lowest accuracy.
Bibliography:326535
ISSN:1330-1136
1846-3908
DOI:10.20532/cit.2024.1005801