Classification of Immunosignature Using Random Forests for Cancer Diagnosis

The non-invasive cancer diagnosis can be considered as one of the most feasible challenges of ground-breaking medicine. Cancer has been characterized as a miscellaneous disease consisting of numerous disparate subtypes. Subsequently, diagnosing and combating cancer is extremely significant. The sign...

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Main Authors:	Zarzar, Mouayad, Razak, Eliza, Htike@Muhammad Yusof, Zaw Zaw, Yusof, Faridah
Format:	Conference or Workshop Item
Language:	English
Published:	2015
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/48060/ http://irep.iium.edu.my/48060/ http://irep.iium.edu.my/48060/1/ID_134.pdf

id	iium-48060
recordtype	eprints
spelling	iium-480602018-05-23T02:33:32Z http://irep.iium.edu.my/48060/ Classification of Immunosignature Using Random Forests for Cancer Diagnosis Zarzar, Mouayad Razak, Eliza Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah T Technology (General) The non-invasive cancer diagnosis can be considered as one of the most feasible challenges of ground-breaking medicine. Cancer has been characterized as a miscellaneous disease consisting of numerous disparate subtypes. Subsequently, diagnosing and combating cancer is extremely significant. The significance of classifying cancer patients has led numerous research parties, from the bioinformatics and the biomedical domains, to rout out the enforcement of data mining methods. The fundamental target of data mining and machine learning is to achieve efficacious cancer classification mechanisms which provide considerable and trustworthy classification accuracy. To attain this essential research purpose, a minimum set of genes that can assure higher performance in classification using data mining algorithms need to be detected. The evolution of authoritative immunofingerprint mining technology is exerting a growing influence on comprehensive cancer diagnosis biology. In this work, we will develop a robust classification model that can be utilized in cancer diagnosis using immunofingerprint data. We have used the Random Subset gene selection method to avoid overfitting and improve model performance in order to make the input data suitable for the classification stage, which has been implemented using the Random Forest (RF) classifier. This novel model has been examined in diagnosing and classifying cancer over two benchmark cancer datasets. Altogether, the empirical results show that the combination of random subset reduction technique with the Random Forest classification method offers a promising tool that is masterful in the diagnosis of carcinoma. 2015 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/48060/1/ID_134.pdf Zarzar, Mouayad and Razak, Eliza and Htike@Muhammad Yusof, Zaw Zaw and Yusof, Faridah (2015) Classification of Immunosignature Using Random Forests for Cancer Diagnosis. In: International Conference on Advances Technology in Telecommunication, Broadcasting, and Satellite, 26-27 September, 2015, Jakarta, Indonesia. (In Press) http://telsatech.org/
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Zarzar, Mouayad Razak, Eliza Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah Classification of Immunosignature Using Random Forests for Cancer Diagnosis
description	The non-invasive cancer diagnosis can be considered as one of the most feasible challenges of ground-breaking medicine. Cancer has been characterized as a miscellaneous disease consisting of numerous disparate subtypes. Subsequently, diagnosing and combating cancer is extremely significant. The significance of classifying cancer patients has led numerous research parties, from the bioinformatics and the biomedical domains, to rout out the enforcement of data mining methods. The fundamental target of data mining and machine learning is to achieve efficacious cancer classification mechanisms which provide considerable and trustworthy classification accuracy. To attain this essential research purpose, a minimum set of genes that can assure higher performance in classification using data mining algorithms need to be detected. The evolution of authoritative immunofingerprint mining technology is exerting a growing influence on comprehensive cancer diagnosis biology. In this work, we will develop a robust classification model that can be utilized in cancer diagnosis using immunofingerprint data. We have used the Random Subset gene selection method to avoid overfitting and improve model performance in order to make the input data suitable for the classification stage, which has been implemented using the Random Forest (RF) classifier. This novel model has been examined in diagnosing and classifying cancer over two benchmark cancer datasets. Altogether, the empirical results show that the combination of random subset reduction technique with the Random Forest classification method offers a promising tool that is masterful in the diagnosis of carcinoma.
format	Conference or Workshop Item
author	Zarzar, Mouayad Razak, Eliza Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah
author_facet	Zarzar, Mouayad Razak, Eliza Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah
author_sort	Zarzar, Mouayad
title	Classification of Immunosignature Using Random Forests for Cancer Diagnosis
title_short	Classification of Immunosignature Using Random Forests for Cancer Diagnosis
title_full	Classification of Immunosignature Using Random Forests for Cancer Diagnosis
title_fullStr	Classification of Immunosignature Using Random Forests for Cancer Diagnosis
title_full_unstemmed	Classification of Immunosignature Using Random Forests for Cancer Diagnosis
title_sort	classification of immunosignature using random forests for cancer diagnosis
publishDate	2015
url	http://irep.iium.edu.my/48060/ http://irep.iium.edu.my/48060/ http://irep.iium.edu.my/48060/1/ID_134.pdf
first_indexed	2023-09-18T21:08:17Z
last_indexed	2023-09-18T21:08:17Z
_version_	1777411080792834048

Classification of Immunosignature Using Random Forests for Cancer Diagnosis

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