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...
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| Language: | English |
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University of Zagreb Faculty of Electrical Engineering and Computing
2024-01-01
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| Series: | Journal of Computing and Information Technology |
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| Online Access: | https://hrcak.srce.hr/file/471976 |
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| author | Ali Nafaa Jaafar |
| author_facet | Ali Nafaa Jaafar |
| author_sort | Ali Nafaa Jaafar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-59f3051bd2564289af7959b95145e6c5 |
| institution | Kabale University |
| issn | 1846-3908 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | University of Zagreb Faculty of Electrical Engineering and Computing |
| record_format | Article |
| series | Journal of Computing and Information Technology |
| spelling | doaj-art-59f3051bd2564289af7959b95145e6c52025-01-09T14:17:14ZengUniversity of Zagreb Faculty of Electrical Engineering and ComputingJournal of Computing and Information Technology1846-39082024-01-0132319521510.20532/cit.2024.1005801Enhancement of Breast Cancer Classification Using Bat Feature Selection with Recurrent Deep LearningAli Nafaa Jaafar0Electrical Engineering Technical College, Middle Technical University, Baghdad, IraqDNA 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.https://hrcak.srce.hr/file/471976RNNLSTMGRUbat algorithmgene expressionfeature selection |
| spellingShingle | Ali Nafaa Jaafar Enhancement of Breast Cancer Classification Using Bat Feature Selection with Recurrent Deep Learning Journal of Computing and Information Technology RNN LSTM GRU bat algorithm gene expression feature selection |
| title | Enhancement of Breast Cancer Classification Using Bat Feature Selection with Recurrent Deep Learning |
| title_full | Enhancement of Breast Cancer Classification Using Bat Feature Selection with Recurrent Deep Learning |
| title_fullStr | Enhancement of Breast Cancer Classification Using Bat Feature Selection with Recurrent Deep Learning |
| title_full_unstemmed | Enhancement of Breast Cancer Classification Using Bat Feature Selection with Recurrent Deep Learning |
| title_short | Enhancement of Breast Cancer Classification Using Bat Feature Selection with Recurrent Deep Learning |
| title_sort | enhancement of breast cancer classification using bat feature selection with recurrent deep learning |
| topic | RNN LSTM GRU bat algorithm gene expression feature selection |
| url | https://hrcak.srce.hr/file/471976 |
| work_keys_str_mv | AT alinafaajaafar enhancementofbreastcancerclassificationusingbatfeatureselectionwithrecurrentdeeplearning |