- author = "Mei Sze Tan and Jing Wei Tan and Siow-Wee Chang and Hwa Jen Yap and Sameem Abdul Kareem and Rosnah Binti Zain",
- title = "A genetic programming approach to oral cancer prognosis",
- journal = "PeerJ",
- year = "2016",
- pages = "e2482",
- month = "21 " # sep,
- keywords = "genetic algorithms, genetic programming, Computational Biology, Oncology, Computational Science, Oral cancer prognosis, Machine learning, Feature selection",
-
URL = "
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5036111/pdf/peerj-04-2482.pdf",
-
DOI = "
doi:10.7717/peerj.2482",
-
abstract = "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. 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.
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.",
- notes = "Bioinformatics Program, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia",
