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

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


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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.

Item Type: Article
Additional Information: ISI Document Delivery No.: 158LD Times Cited: 0 Cited Reference Count: 40 Chang, Siow-Wee Abdul-Kareem, Sameem Merican, Amir Feisal Zain, Rosnah Binti University of Malaya Research Grant (UMRG) RG026-09ICT This study is supported by the University of Malaya Research Grant (UMRG) with the project number RG026-09ICT. The authors would like to thank Dr Mannil Thomas Abraham from the Tunku Ampuan Rahimah Hospital, Ministry of Health, Malaysia, Dr Thomas George Kallarakkal from the Department of Oral Pathology and Oral Medicine and Periodontology, the staff from the Oral & Maxillofacial Surgery department, the Oral Pathology Diagnostic Laboratory, the OCRCC, the Faculty of Dentistry, and the ENT department, Faculty of Medicine, University of Malaya for the preparation of the dataset and the related data and documents for this project. Biomed central ltd London
Uncontrolled Keywords: Oral cancer prognosis Clinicopathologic Genomic Feature selection Machine learning squamous-cell carcinoma differential expression survival analysis tumor thickness breast-cancer p63 neck head p53 progression
Subjects: R Medicine > RK Dentistry
Divisions: Faculty of Dentistry > Dept of Oral Pathology & Oral Medicine & Periodontology
Depositing User: Mr Ahmad Azwan Azman
Date Deposited: 17 Jul 2013 00:18
Last Modified: 15 May 2019 07:15
URI: http://eprints.um.edu.my/id/eprint/8124

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