A genetic programming approach to oral cancer prognosis.
BACKGROUND
The potential of genetic programming (GP) on various fields has been attained in recent years. In bio-medical field, many researches in GP are focused on the recognition of cancerous cells and also on gene expression profiling data. In this research, the aim is to study the performance of GP on the survival prediction of a small sample size of oral cancer prognosis dataset, which is the first study in the field of oral cancer prognosis.


METHODS
GP is applied on an oral cancer dataset that contains 31 cases collected from the Malaysia Oral Cancer Database and Tissue Bank System (MOCDTBS). The feature subsets that is automatically selected through GP were noted and the influences of this subset on the results of GP were recorded. In addition, a comparison between the GP performance and that of the Support Vector Machine (SVM) and logistic regression (LR) are also done in order to verify the predictive capabilities of the GP.


RESULTS
The result shows that GP performed the best (average accuracy of 83.87% and average AUROC of 0.8341) when the features selected are smoking, drinking, chewing, histological differentiation of SCC, and oncogene p63. In addition, based on the comparison results, we found that the GP outperformed the SVM and LR in oral cancer prognosis.


CONCLUSIONS
Some of the features in the dataset are found to be statistically co-related. This is because the accuracy of the GP prediction drops when one of the feature in the best feature subset is excluded. Thus, GP provides an automatic feature selection function, which chooses features that are highly correlated to the prognosis of oral cancer. This makes GP an ideal prediction model for cancer clinical and genomic data that can be used to aid physicians in their decision making stage of diagnosis or prognosis.
Bioinformatics Program, Institute of Biological Sciences, Faculty of Science, University of Malaya , Kuala Lumpur , Malaysia.
Mei Sze
MS
Tan
Bioinformatics Program, Institute of Biological Sciences, Faculty of Science, University of Malaya , Kuala Lumpur , Malaysia.
Jing Wei
JW
Tan
Bioinformatics Program, Institute of Biological Sciences, Faculty of Science, University of Malaya , Kuala Lumpur , Malaysia.
Siow-Wee
SW
Chang
Department of Mechanical Engineering, Faculty of Engineering, University of Malaya , Kuala Lumpur , Malaysia.
Hwa Jen
HJ
Yap
Department of Artificial Intelligence, Faculty of Computer Science & Information Technology, University of Malaya , Kuala Lumpur , Malaysia.
Sameem
S
Abdul Kareem
Oral Cancer Research & Coordinating Centre (OCRCC), Faculty of Dentistry, University of Malaya , Kuala Lumpur , Malaysia.
Rosnah Binti
RB
Zain
Feature selection; Genetic Programming; Machine learning; Oral cancer prognosis
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