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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| Vydané v: | Journal of computing and information technology Ročník 32; číslo 3; s. 195 - 215 |
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| Médium: | Journal Article Paper |
| Jazyk: | English |
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Sveuciliste U Zagrebu
01.09.2024
Sveučilište u Zagrebu Fakultet elektrotehnike i računarstva University of Zagreb Faculty of Electrical Engineering and Computing |
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| ISSN: | 1330-1136, 1846-3908 |
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| Abstract | 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. |
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| AbstractList | 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. ACM CCS (2012) Classification: Computing methodologies [right arrow] Machine learning [right arrow] Machine learning approaches [right arrow] Neural networks Applied computing [right arrow] Life and medical sciences [right arrow] Genomics [right arrow] Computational genomics Keywords: RNN, LSTM, GRU, bat algorithm, gene expression, feature selection 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. |
| Audience | Academic |
| Author | Jaafar, Ali Nafaa |
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| Snippet | 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... |
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| SubjectTerms | 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 |
| Title | Enhancement of Breast Cancer Classification Using Bat Feature Selection with Recurrent Deep Learning |
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