A Novel Hybrid Runge Kutta Optimizer with Support Vector Machine on Gene Expression Data for Cancer Classification
It is crucial to accurately categorize cancers using microarray data. Researchers have employed a variety of computational intelligence approaches to analyze gene expression data. It is believed that the most difficult part of the problem of cancer diagnosis is determining which genes are informativ...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/9/1621 |
| _version_ | 1797602765131743232 |
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| author | Essam H. Houssein Hager N. Hassan Nagwan Abdel Samee Mona M. Jamjoom |
| author_facet | Essam H. Houssein Hager N. Hassan Nagwan Abdel Samee Mona M. Jamjoom |
| author_sort | Essam H. Houssein |
| collection | DOAJ |
| description | It is crucial to accurately categorize cancers using microarray data. Researchers have employed a variety of computational intelligence approaches to analyze gene expression data. It is believed that the most difficult part of the problem of cancer diagnosis is determining which genes are informative. Therefore, selecting genes to study as a starting point for cancer classification is common practice. We offer a novel approach that combines the Runge Kutta optimizer (RUN) with a support vector machine (SVM) as the classifier to select the significant genes in the detection of cancer tissues. As a means of dealing with the high dimensionality that characterizes microarray datasets, the preprocessing stage of the ReliefF method is implemented. The proposed RUN–SVM approach is tested on binary-class microarray datasets (Breast2 and Prostate) and multi-class microarray datasets in order to assess its efficacy (i.e., Brain Tumor1, Brain Tumor2, Breast3, and Lung Cancer). Based on the experimental results obtained from analyzing six different cancer gene expression datasets, the proposed RUN–SVM approach was found to statistically beat the other competing algorithms due to its innovative search technique. |
| first_indexed | 2024-03-11T04:21:15Z |
| format | Article |
| id | doaj.art-8053b3e8148b4221abf41dee6310c620 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T04:21:15Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-8053b3e8148b4221abf41dee6310c6202023-11-17T22:46:11ZengMDPI AGDiagnostics2075-44182023-05-01139162110.3390/diagnostics13091621A Novel Hybrid Runge Kutta Optimizer with Support Vector Machine on Gene Expression Data for Cancer ClassificationEssam H. Houssein0Hager N. Hassan1Nagwan Abdel Samee2Mona M. Jamjoom3Faculty of Computers and Information, Minia University, Minia 61519, EgyptFaculty of Computers and Information, Minia University, Minia 61519, EgyptDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi ArabiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi ArabiaIt is crucial to accurately categorize cancers using microarray data. Researchers have employed a variety of computational intelligence approaches to analyze gene expression data. It is believed that the most difficult part of the problem of cancer diagnosis is determining which genes are informative. Therefore, selecting genes to study as a starting point for cancer classification is common practice. We offer a novel approach that combines the Runge Kutta optimizer (RUN) with a support vector machine (SVM) as the classifier to select the significant genes in the detection of cancer tissues. As a means of dealing with the high dimensionality that characterizes microarray datasets, the preprocessing stage of the ReliefF method is implemented. The proposed RUN–SVM approach is tested on binary-class microarray datasets (Breast2 and Prostate) and multi-class microarray datasets in order to assess its efficacy (i.e., Brain Tumor1, Brain Tumor2, Breast3, and Lung Cancer). Based on the experimental results obtained from analyzing six different cancer gene expression datasets, the proposed RUN–SVM approach was found to statistically beat the other competing algorithms due to its innovative search technique.https://www.mdpi.com/2075-4418/13/9/1621feature selectionRunge Kutta optimizermicroarraygene expressionsupport vector machinescancer classification |
| spellingShingle | Essam H. Houssein Hager N. Hassan Nagwan Abdel Samee Mona M. Jamjoom A Novel Hybrid Runge Kutta Optimizer with Support Vector Machine on Gene Expression Data for Cancer Classification Diagnostics feature selection Runge Kutta optimizer microarray gene expression support vector machines cancer classification |
| title | A Novel Hybrid Runge Kutta Optimizer with Support Vector Machine on Gene Expression Data for Cancer Classification |
| title_full | A Novel Hybrid Runge Kutta Optimizer with Support Vector Machine on Gene Expression Data for Cancer Classification |
| title_fullStr | A Novel Hybrid Runge Kutta Optimizer with Support Vector Machine on Gene Expression Data for Cancer Classification |
| title_full_unstemmed | A Novel Hybrid Runge Kutta Optimizer with Support Vector Machine on Gene Expression Data for Cancer Classification |
| title_short | A Novel Hybrid Runge Kutta Optimizer with Support Vector Machine on Gene Expression Data for Cancer Classification |
| title_sort | novel hybrid runge kutta optimizer with support vector machine on gene expression data for cancer classification |
| topic | feature selection Runge Kutta optimizer microarray gene expression support vector machines cancer classification |
| url | https://www.mdpi.com/2075-4418/13/9/1621 |
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