Cargando…
A Dual Level Analysis with Evolutionary Computing and Swarm Models for Classification of Leukemia
One of the major reasons of mortality in human beings is cancer, and there is an absolute necessity for doctors to identify and treat a person suffering from it. Leukemia is a group of blood cancers that usually originates in the bone marrow and results in very high number of abnormal cells. For the...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9162867/ https://www.ncbi.nlm.nih.gov/pubmed/35663047 http://dx.doi.org/10.1155/2022/2052061 |
_version_ | 1784719803146043392 |
---|---|
author | Prabhakar, Sunil Kumar Ryu, Semin Jeong, In cheol Won, Dong-Ok |
author_facet | Prabhakar, Sunil Kumar Ryu, Semin Jeong, In cheol Won, Dong-Ok |
author_sort | Prabhakar, Sunil Kumar |
collection | PubMed |
description | One of the major reasons of mortality in human beings is cancer, and there is an absolute necessity for doctors to identify and treat a person suffering from it. Leukemia is a group of blood cancers that usually originates in the bone marrow and results in very high number of abnormal cells. For the diagnosis of cancer, microarray data serves as an important clinical application and serves as a great aid to the entire medical community. The dimensionality of the microarray data is too high, and so selection of suitable genes is quite an important step for the improvement of data classification. Therefore, for the prediction and diagnosis of cancer, there is an utmost necessity to select the most informative genes. In this work, Minimum Redundancy Maximum Relevance (MRMR), Signal to Noise Ratio (SNR), Multivariate Error Weight Uncorrelated Shrunken Centroid (EWUSC), and multivariate correlation-based feature selection (CFS) are chosen as initial feature selection techniques. Then, to select the most informative genes, five different kinds of evolutionary optimization techniques too are incorporated here such as African Buffalo Optimization (ABO), Artificial Bee Colony Optimization (ABCO), Cockroach Swarm Optimization (CSO), Imperialist Competitive Optimization (ICO), and Social Spider Optimization (SSO). Finally, the optimized values are fed through classification process and the best results are obtained when multivariate CFS with SSO is utilized and classified with Probabilistic Neural Network (PNN), and a high classification accuracy of 95.70% is obtained. |
format | Online Article Text |
id | pubmed-9162867 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-91628672022-06-03 A Dual Level Analysis with Evolutionary Computing and Swarm Models for Classification of Leukemia Prabhakar, Sunil Kumar Ryu, Semin Jeong, In cheol Won, Dong-Ok Biomed Res Int Research Article One of the major reasons of mortality in human beings is cancer, and there is an absolute necessity for doctors to identify and treat a person suffering from it. Leukemia is a group of blood cancers that usually originates in the bone marrow and results in very high number of abnormal cells. For the diagnosis of cancer, microarray data serves as an important clinical application and serves as a great aid to the entire medical community. The dimensionality of the microarray data is too high, and so selection of suitable genes is quite an important step for the improvement of data classification. Therefore, for the prediction and diagnosis of cancer, there is an utmost necessity to select the most informative genes. In this work, Minimum Redundancy Maximum Relevance (MRMR), Signal to Noise Ratio (SNR), Multivariate Error Weight Uncorrelated Shrunken Centroid (EWUSC), and multivariate correlation-based feature selection (CFS) are chosen as initial feature selection techniques. Then, to select the most informative genes, five different kinds of evolutionary optimization techniques too are incorporated here such as African Buffalo Optimization (ABO), Artificial Bee Colony Optimization (ABCO), Cockroach Swarm Optimization (CSO), Imperialist Competitive Optimization (ICO), and Social Spider Optimization (SSO). Finally, the optimized values are fed through classification process and the best results are obtained when multivariate CFS with SSO is utilized and classified with Probabilistic Neural Network (PNN), and a high classification accuracy of 95.70% is obtained. Hindawi 2022-05-26 /pmc/articles/PMC9162867/ /pubmed/35663047 http://dx.doi.org/10.1155/2022/2052061 Text en Copyright © 2022 Sunil Kumar Prabhakar et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Prabhakar, Sunil Kumar Ryu, Semin Jeong, In cheol Won, Dong-Ok A Dual Level Analysis with Evolutionary Computing and Swarm Models for Classification of Leukemia |
title | A Dual Level Analysis with Evolutionary Computing and Swarm Models for Classification of Leukemia |
title_full | A Dual Level Analysis with Evolutionary Computing and Swarm Models for Classification of Leukemia |
title_fullStr | A Dual Level Analysis with Evolutionary Computing and Swarm Models for Classification of Leukemia |
title_full_unstemmed | A Dual Level Analysis with Evolutionary Computing and Swarm Models for Classification of Leukemia |
title_short | A Dual Level Analysis with Evolutionary Computing and Swarm Models for Classification of Leukemia |
title_sort | dual level analysis with evolutionary computing and swarm models for classification of leukemia |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9162867/ https://www.ncbi.nlm.nih.gov/pubmed/35663047 http://dx.doi.org/10.1155/2022/2052061 |
work_keys_str_mv | AT prabhakarsunilkumar aduallevelanalysiswithevolutionarycomputingandswarmmodelsforclassificationofleukemia AT ryusemin aduallevelanalysiswithevolutionarycomputingandswarmmodelsforclassificationofleukemia AT jeongincheol aduallevelanalysiswithevolutionarycomputingandswarmmodelsforclassificationofleukemia AT wondongok aduallevelanalysiswithevolutionarycomputingandswarmmodelsforclassificationofleukemia AT prabhakarsunilkumar duallevelanalysiswithevolutionarycomputingandswarmmodelsforclassificationofleukemia AT ryusemin duallevelanalysiswithevolutionarycomputingandswarmmodelsforclassificationofleukemia AT jeongincheol duallevelanalysiswithevolutionarycomputingandswarmmodelsforclassificationofleukemia AT wondongok duallevelanalysiswithevolutionarycomputingandswarmmodelsforclassificationofleukemia |