Implementation of Support Vector Machine - Recursive Feature Elimination for MicroRNA Selection in Breast Cancer Classification
Breast cancer is the most frequent cancer caused death among women. An attempt to reduce death cases caused by breast cancer, was to detect cancer cells when it still in early stage. MicroRNA is one of the biomarker for cancer that can be used to detect cancer cell even in its early stage. However,...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Departement of Electrical Engineering, Faculty of Engineering, Universitas Brawijaya
2020-04-01
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| Series: | Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) |
| Subjects: | |
| Online Access: | https://jurnaleeccis.ub.ac.id/index.php/eeccis/article/view/602 |
| _version_ | 1826933705142173696 |
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| author | Ratih Permatasari Adi Wibowo |
| author_facet | Ratih Permatasari Adi Wibowo |
| author_sort | Ratih Permatasari |
| collection | DOAJ |
| description | Breast cancer is the most frequent cancer caused death among women. An attempt to reduce death cases caused by breast cancer, was to detect cancer cells when it still in early stage. MicroRNA is one of the biomarker for cancer that can be used to detect cancer cell even in its early stage. However, MicroRNA data tends to have thousand types of expression which required a lot of costs if it examined one by one thoroughly. Feature selection method can be used to extract important MicroRNAs that support clasification process between normal people and people with breast cancer. Support Vector Recursive Feature Elimination (SVM-RFE) is one of the feature selection method that can be used to select MicroRNA data. This research aims to produce the best smallest subset that contains selected MicroRNA expressions using the SVM-RFE as feature selection method. This experiment result showed that the best selected subset was able to provide 99% classification accuracy with only 3 MicroRNA expressions, where 2 from 3 selected MicroRNA hold potential as a biomarker of breast cancer. |
| first_indexed | 2024-03-11T13:25:47Z |
| format | Article |
| id | doaj.art-c872e42460d44127aaf9d9e39399281c |
| institution | Directory Open Access Journal |
| issn | 2460-8122 |
| language | English |
| last_indexed | 2025-02-17T17:35:44Z |
| publishDate | 2020-04-01 |
| publisher | Departement of Electrical Engineering, Faculty of Engineering, Universitas Brawijaya |
| record_format | Article |
| series | Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) |
| spelling | doaj.art-c872e42460d44127aaf9d9e39399281c2024-12-14T10:55:12ZengDepartement of Electrical Engineering, Faculty of Engineering, Universitas BrawijayaJurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems)2460-81222020-04-011411510.21776/jeeccis.v14i1.602398Implementation of Support Vector Machine - Recursive Feature Elimination for MicroRNA Selection in Breast Cancer ClassificationRatih Permatasari0Adi Wibowo1Universitas DiponegoroUniversitas DiponegoroBreast cancer is the most frequent cancer caused death among women. An attempt to reduce death cases caused by breast cancer, was to detect cancer cells when it still in early stage. MicroRNA is one of the biomarker for cancer that can be used to detect cancer cell even in its early stage. However, MicroRNA data tends to have thousand types of expression which required a lot of costs if it examined one by one thoroughly. Feature selection method can be used to extract important MicroRNAs that support clasification process between normal people and people with breast cancer. Support Vector Recursive Feature Elimination (SVM-RFE) is one of the feature selection method that can be used to select MicroRNA data. This research aims to produce the best smallest subset that contains selected MicroRNA expressions using the SVM-RFE as feature selection method. This experiment result showed that the best selected subset was able to provide 99% classification accuracy with only 3 MicroRNA expressions, where 2 from 3 selected MicroRNA hold potential as a biomarker of breast cancer.https://jurnaleeccis.ub.ac.id/index.php/eeccis/article/view/602breast cancerfeature selectionmicrornasvm-rfe |
| spellingShingle | Ratih Permatasari Adi Wibowo Implementation of Support Vector Machine - Recursive Feature Elimination for MicroRNA Selection in Breast Cancer Classification Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) breast cancer feature selection microrna svm-rfe |
| title | Implementation of Support Vector Machine - Recursive Feature Elimination for MicroRNA Selection in Breast Cancer Classification |
| title_full | Implementation of Support Vector Machine - Recursive Feature Elimination for MicroRNA Selection in Breast Cancer Classification |
| title_fullStr | Implementation of Support Vector Machine - Recursive Feature Elimination for MicroRNA Selection in Breast Cancer Classification |
| title_full_unstemmed | Implementation of Support Vector Machine - Recursive Feature Elimination for MicroRNA Selection in Breast Cancer Classification |
| title_short | Implementation of Support Vector Machine - Recursive Feature Elimination for MicroRNA Selection in Breast Cancer Classification |
| title_sort | implementation of support vector machine recursive feature elimination for microrna selection in breast cancer classification |
| topic | breast cancer feature selection microrna svm-rfe |
| url | https://jurnaleeccis.ub.ac.id/index.php/eeccis/article/view/602 |
| work_keys_str_mv | AT ratihpermatasari implementationofsupportvectormachinerecursivefeatureeliminationformicrornaselectioninbreastcancerclassification AT adiwibowo implementationofsupportvectormachinerecursivefeatureeliminationformicrornaselectioninbreastcancerclassification |