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Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods

Background: Machine learning techniques are becoming useful as an alternative approach to conventional medical diagnosis or prognosis as they are good for handling noisy and incomplete data, and significant results can be attained despite a small sample size. Traditionally, clinicians make prognosti...

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Main Authors: Chang, S.W., Abdul-Kareem, S., Merican, A.F., Zain, R.B.
Format: Article
Language:English
Published: BioMed Central 2013
Subjects:
RK Dentistry
Online Access:http://eprints.um.edu.my/8124/1/Oral_cancer_prognosis_based_on_clinicopathologic.pdf
http://eprints.um.edu.my/8124/
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spelling my.um.eprints.81242019-05-15T07:15:35Z http://eprints.um.edu.my/8124/ Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods Chang, S.W. Abdul-Kareem, S. Merican, A.F. Zain, R.B. RK Dentistry Background: Machine learning techniques are becoming useful as an alternative approach to conventional medical diagnosis or prognosis as they are good for handling noisy and incomplete data, and significant results can be attained despite a small sample size. Traditionally, clinicians make prognostic decisions based on clinicopathologic markers. However, it is not easy for the most skilful clinician to come out with an accurate prognosis by using these markers alone. Thus, there is a need to use genomic markers to improve the accuracy of prognosis. The main aim of this research is to apply a hybrid of feature selection and machine learning methods in oral cancer prognosis based on the parameters of the correlation of clinicopathologic and genomic markers. Results: In the first stage of this research, five feature selection methods have been proposed and experimented on the oral cancer prognosis dataset. In the second stage, the model with the features selected from each feature selection methods are tested on the proposed classifiers. Four types of classifiers are chosen; these are namely, ANFIS, artificial neural network, support vector machine and logistic regression. A k-fold cross-validation is implemented on all types of classifiers due to the small sample size. The hybrid model of ReliefF-GA-ANFIS with 3- input features of drink, invasion and p63 achieved the best accuracy (accuracy = 93.81; AUC = 0.90) for the oral cancer prognosis. Conclusions: The results revealed that the prognosis is superior with the presence of both clinicopathologic and genomic markers. The selected features can be investigated further to validate the potential of becoming as significant prognostic signature in the oral cancer studies. BioMed Central 2013 Article PeerReviewed application/pdf en http://eprints.um.edu.my/8124/1/Oral_cancer_prognosis_based_on_clinicopathologic.pdf Chang, S.W. and Abdul-Kareem, S. and Merican, A.F. and Zain, R.B. (2013) Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods. BMC Bioinformatics, 14. ISSN 1471-2105
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
language English
topic RK Dentistry
spellingShingle RK Dentistry
Chang, S.W.
Abdul-Kareem, S.
Merican, A.F.
Zain, R.B.
Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods
description Background: Machine learning techniques are becoming useful as an alternative approach to conventional medical diagnosis or prognosis as they are good for handling noisy and incomplete data, and significant results can be attained despite a small sample size. Traditionally, clinicians make prognostic decisions based on clinicopathologic markers. However, it is not easy for the most skilful clinician to come out with an accurate prognosis by using these markers alone. Thus, there is a need to use genomic markers to improve the accuracy of prognosis. The main aim of this research is to apply a hybrid of feature selection and machine learning methods in oral cancer prognosis based on the parameters of the correlation of clinicopathologic and genomic markers. Results: In the first stage of this research, five feature selection methods have been proposed and experimented on the oral cancer prognosis dataset. In the second stage, the model with the features selected from each feature selection methods are tested on the proposed classifiers. Four types of classifiers are chosen; these are namely, ANFIS, artificial neural network, support vector machine and logistic regression. A k-fold cross-validation is implemented on all types of classifiers due to the small sample size. The hybrid model of ReliefF-GA-ANFIS with 3- input features of drink, invasion and p63 achieved the best accuracy (accuracy = 93.81; AUC = 0.90) for the oral cancer prognosis. Conclusions: The results revealed that the prognosis is superior with the presence of both clinicopathologic and genomic markers. The selected features can be investigated further to validate the potential of becoming as significant prognostic signature in the oral cancer studies.
format Article
author Chang, S.W.
Abdul-Kareem, S.
Merican, A.F.
Zain, R.B.
author_facet Chang, S.W.
Abdul-Kareem, S.
Merican, A.F.
Zain, R.B.
author_sort Chang, S.W.
title Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods
title_short Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods
title_full Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods
title_fullStr Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods
title_full_unstemmed Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods
title_sort oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods
publisher BioMed Central
publishDate 2013
url http://eprints.um.edu.my/8124/1/Oral_cancer_prognosis_based_on_clinicopathologic.pdf
http://eprints.um.edu.my/8124/
_version_ 1643688223169314816
score 13.211869

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