التفاصيل البيبلوغرافية
| العنوان: |
A Survey on Acute Leukemia Expression Data Classification Using Ensembles. |
| المؤلفون: |
Zaied, Abdel Nasser H., Rushdy, Ehab, Gamal, Mona |
| المصدر: |
Computer Systems Science & Engineering; 2023, Vol. 47 Issue 2, p1349-1364, 16p |
| مصطلحات موضوعية: |
ACUTE leukemia, GENE expression, MICROARRAY technology, COMPUTER algorithms, MORTALITY |
| مستخلص: |
Acute leukemia is an aggressive disease that has high mortality rates worldwide. The error rate can be as high as 40% when classifying acute leukemia into its subtypes. So, there is an urgent need to support hematologists during the classification process. More than two decades ago, researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test case. The high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression data. Ensemble machine learning is an effective method that combines individual classifiers to classify new samples. Ensemble classifiers are recognized as powerful algorithms with numerous advantages over traditional classifiers. Over the past few decades, researchers have focused a great deal of attention on ensemble classifiers in a wide variety of fields, including but not limited to disease diagnosis, finance, bioinformatics, healthcare, manufacturing, and geography. This paper reviews the recent ensemble classifier approaches utilized for acute leukemia gene expression data classification. Moreover, a framework for classifying acute leukemia gene expression data is proposed. The pairwise correlation gene selection method and the Rotation Forest of Bayesian Networks are both used in this framework. Experimental outcomes show that the classification accuracy achieved by the acute leukemia ensemble classifiers constructed according to the suggested framework is good compared to the classification accuracy achieved in other studies. [ABSTRACT FROM AUTHOR] |
|
Copyright of Computer Systems Science & Engineering is the property of Tech Science Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Supplemental Index |
