A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation
Breast cancer is one of the leading causes of death and most frequently diagnosed cancer amongst women. Annually, almost half a million women do not survive the disease and die from breast cancer. Machine learning is a subfield of artificial intelligence (AI) and computer science that uses data and...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Institute of Advanced Engineering and Science
2023
|
| Online Access: | http://eprints.utem.edu.my/id/eprint/26481/2/4838-13012-1-PB.PDF http://eprints.utem.edu.my/id/eprint/26481/ https://beei.org/index.php/EEI/article/view/4838/3270 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.utem.eprints.26481 |
|---|---|
| record_format |
eprints |
| spelling |
my.utem.eprints.264812023-02-28T08:03:58Z http://eprints.utem.edu.my/id/eprint/26481/ A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation Mohd Ali, Nursabillilah Besar, Rosli Ab Aziz, Nor Azlina Breast cancer is one of the leading causes of death and most frequently diagnosed cancer amongst women. Annually, almost half a million women do not survive the disease and die from breast cancer. Machine learning is a subfield of artificial intelligence (AI) and computer science that uses data and algorithms to mimic how humans learn, and gradually improving its accuracy. In this work, simple machine learning methods are used to classify breast cancer microarray data to normal and relapse. The data is from the gene expression omnibus (GEO) website namely GSE45255 and GSE15852. These two datasets are integrated and combined to form a single dataset. The study involved three machine learning algorithms, random forest (RF), extra tree (ET), and support vector machine (SVM). Grid search cross validation (CV) is applied for hyperparameter tuning of the algorithms. The result shows that the tuned SVM is best among the tested algorithms with accuracy of 97.78%. In the future it is recommended to include feature selection method to get the optimal features and better classification accuracies. Institute of Advanced Engineering and Science 2023-04 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/26481/2/4838-13012-1-PB.PDF Mohd Ali, Nursabillilah and Besar, Rosli and Ab Aziz, Nor Azlina (2023) A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation. Bulletin of Electrical Engineering and Informatics, 12 (2). pp. 1047-1054. ISSN 2302-9285 https://beei.org/index.php/EEI/article/view/4838/3270 10.11591/eei.v12i2.4838 |
| institution |
Universiti Teknikal Malaysia Melaka |
| building |
UTEM Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Teknikal Malaysia Melaka |
| content_source |
UTEM Institutional Repository |
| url_provider |
http://eprints.utem.edu.my/ |
| language |
English |
| description |
Breast cancer is one of the leading causes of death and most frequently diagnosed cancer amongst women. Annually, almost half a million women do not survive the disease and die from breast cancer. Machine learning is a subfield of artificial intelligence (AI) and computer science that uses data and algorithms to mimic how humans learn, and gradually improving its accuracy. In this work, simple machine learning methods are used to classify breast cancer microarray data to normal and relapse. The data is from the gene expression omnibus (GEO) website namely GSE45255 and GSE15852. These two datasets are integrated and combined to form a single dataset. The study involved three machine learning algorithms, random forest (RF), extra tree (ET), and support vector machine (SVM). Grid search cross validation (CV) is applied for hyperparameter tuning of the algorithms. The result shows that the tuned SVM is best among the tested algorithms with accuracy of 97.78%. In the future it is recommended to include feature selection method to get the optimal features and better classification accuracies. |
| format |
Article |
| author |
Mohd Ali, Nursabillilah Besar, Rosli Ab Aziz, Nor Azlina |
| spellingShingle |
Mohd Ali, Nursabillilah Besar, Rosli Ab Aziz, Nor Azlina A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation |
| author_facet |
Mohd Ali, Nursabillilah Besar, Rosli Ab Aziz, Nor Azlina |
| author_sort |
Mohd Ali, Nursabillilah |
| title |
A case study of microarray breast cancer classification using
machine learning algorithms with grid search cross validation |
| title_short |
A case study of microarray breast cancer classification using
machine learning algorithms with grid search cross validation |
| title_full |
A case study of microarray breast cancer classification using
machine learning algorithms with grid search cross validation |
| title_fullStr |
A case study of microarray breast cancer classification using
machine learning algorithms with grid search cross validation |
| title_full_unstemmed |
A case study of microarray breast cancer classification using
machine learning algorithms with grid search cross validation |
| title_sort |
case study of microarray breast cancer classification using
machine learning algorithms with grid search cross validation |
| publisher |
Institute of Advanced Engineering and Science |
| publishDate |
2023 |
| url |
http://eprints.utem.edu.my/id/eprint/26481/2/4838-13012-1-PB.PDF http://eprints.utem.edu.my/id/eprint/26481/ https://beei.org/index.php/EEI/article/view/4838/3270 |
| _version_ |
1759693074133417984 |
| score |
13.211869 |