Cancer recurrence prediction using machine learning

Cancer is one of the deadliest diseases in the world and is responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Despite the fact that cancer is preventable and curable in early stages, the vast majority of patients are diagnosed wit...

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Main Authors:	Shoon Lei, Win, Htike@Muhammad Yusof, Zaw Zaw, Yusof, Faridah, Noorbatcha, Ibrahim Ali
Format:	Article
Language:	English
Published:	AIRCC Publishing Corporation 2014
Subjects:	Q Science (General)
Online Access:	http://irep.iium.edu.my/37773/ http://irep.iium.edu.my/37773/ http://irep.iium.edu.my/37773/ http://irep.iium.edu.my/37773/1/2214ijcsity02.pdf

id	iium-37773
recordtype	eprints
spelling	iium-377732018-06-12T02:32:50Z http://irep.iium.edu.my/37773/ Cancer recurrence prediction using machine learning Shoon Lei, Win Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah Noorbatcha, Ibrahim Ali Q Science (General) Cancer is one of the deadliest diseases in the world and is responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Despite the fact that cancer is preventable and curable in early stages, the vast majority of patients are diagnosed with cancer very late. Furthermore, cancer commonly comes back after years of treatment. Therefore, it is of paramount importance to predict cancer recurrence so that specific treatments can be sought. Nonetheless, conventional methods of predicting cancer recurrence rely solely on histopathology and the results are not very reliable. The microarray gene expression technology is a promising technology that could predict cancer recurrence by analyzing the gene expression of sample cells. The microarray technology allows researchers to examine the expression of thousands of genes simultaneously. This paper describes a state-of-the-art machine learning based approach called averaged one-dependence estimators with subsumption resolution to tackle the problem of predicting, from DNA microarray gene expression data, whether a particular cancer will recur within a specific timeframe, which is usually 5 years. To lower the computational complexity, we employ an entropy-based gene selection approach to select relevant prognostic genes that are directly responsible for recurrence prediction. This proposed system has achieved an average accuracy of 98.9% in predicting cancer recurrence over 3 datasets. The experimental results demonstrate the efficacy of our framework. AIRCC Publishing Corporation 2014-05 Article PeerReviewed application/pdf en http://irep.iium.edu.my/37773/1/2214ijcsity02.pdf Shoon Lei, Win and Htike@Muhammad Yusof, Zaw Zaw and Yusof, Faridah and Noorbatcha, Ibrahim Ali (2014) Cancer recurrence prediction using machine learning. International Journal of Computational Science and Information Technology (IJCSITY), 2 (2). pp. 11-20. ISSN 2320-8457 (P) 2320-7442 (O) http://airccse.org/journal/ijcsity/Current2014.html 10.5121/ijcsity.2014.2202
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	Q Science (General)
spellingShingle	Q Science (General) Shoon Lei, Win Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah Noorbatcha, Ibrahim Ali Cancer recurrence prediction using machine learning
description	Cancer is one of the deadliest diseases in the world and is responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Despite the fact that cancer is preventable and curable in early stages, the vast majority of patients are diagnosed with cancer very late. Furthermore, cancer commonly comes back after years of treatment. Therefore, it is of paramount importance to predict cancer recurrence so that specific treatments can be sought. Nonetheless, conventional methods of predicting cancer recurrence rely solely on histopathology and the results are not very reliable. The microarray gene expression technology is a promising technology that could predict cancer recurrence by analyzing the gene expression of sample cells. The microarray technology allows researchers to examine the expression of thousands of genes simultaneously. This paper describes a state-of-the-art machine learning based approach called averaged one-dependence estimators with subsumption resolution to tackle the problem of predicting, from DNA microarray gene expression data, whether a particular cancer will recur within a specific timeframe, which is usually 5 years. To lower the computational complexity, we employ an entropy-based gene selection approach to select relevant prognostic genes that are directly responsible for recurrence prediction. This proposed system has achieved an average accuracy of 98.9% in predicting cancer recurrence over 3 datasets. The experimental results demonstrate the efficacy of our framework.
format	Article
author	Shoon Lei, Win Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah Noorbatcha, Ibrahim Ali
author_facet	Shoon Lei, Win Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah Noorbatcha, Ibrahim Ali
author_sort	Shoon Lei, Win
title	Cancer recurrence prediction using machine learning
title_short	Cancer recurrence prediction using machine learning
title_full	Cancer recurrence prediction using machine learning
title_fullStr	Cancer recurrence prediction using machine learning
title_full_unstemmed	Cancer recurrence prediction using machine learning
title_sort	cancer recurrence prediction using machine learning
publisher	AIRCC Publishing Corporation
publishDate	2014
url	http://irep.iium.edu.my/37773/ http://irep.iium.edu.my/37773/ http://irep.iium.edu.my/37773/ http://irep.iium.edu.my/37773/1/2214ijcsity02.pdf
first_indexed	2023-09-18T20:54:09Z
last_indexed	2023-09-18T20:54:09Z
_version_	1777410192169762816

Cancer recurrence prediction using machine learning

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