Created by W.Langdon from gp-bibliography.bib Revision:1.6561
Method. 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.
Result. The result shows that GP performed the best (average accuracy of 83.87percent 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.
Discussion. 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.",
Genetic Programming entries for Mei Sze Tan Jing Wei Tan Siow-Wee Chang Hwa Jen Yap Sameem Binti Abdul Kareem Rosnah Binti Mohd Zain