Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods
Open Access
- 31 May 2013
- journal article
- research article
- Published by Springer Science and Business Media LLC in BMC Bioinformatics
- Vol. 14 (1), 1-15
- https://doi.org/10.1186/1471-2105-14-170
Abstract
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. 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. 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.This publication has 32 references indexed in Scilit:
- A multiscale and multiparametric approach for modeling the progression of oral cancerBMC Medical Informatics and Decision Making, 2012
- Prognostic factors and survival analysis in a sample of oral squamous cell carcinoma patientsOral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology, 2008
- Improved breast cancer prognosis through the combination of clinical and genetic markersBioinformatics, 2006
- Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networksBioinformatics, 2006
- Neuro-Fuzzy Modeling: An Accurate and Interpretable Method for Predicting Bladder Cancer ProgressionJournal of Urology, 2006
- Function and Importance of p63 in Normal Oral Mucosa and Squamous Cell Carcinoma of the Head and NeckAdvances in oto-rhino-laryngology, 2004
- Genetic mechanisms in squamous cell carcinoma of the head and neckOral Oncology, 2001
- Introductory StatisticsThe American Statistician, 1997
- ANFIS: adaptive-network-based fuzzy inference systemIEEE Transactions on Systems, Man, and Cybernetics, 1993
- Estimating the Error Rate of a Prediction Rule: Improvement on Cross-ValidationJournal of the American Statistical Association, 1983