The application of artificial intelligent techniques in oral cancer prognosis based on clinicopathologic and genomic markers / Chang Siow Wee - Chang, Siow Wee (2013)
Artificial intelligent (AI) techniques are becoming useful as an alternative approach to conventional medical diagnosis or prognosis. AI techniques are good for handling noisy and incomplete data, and significant results can be attained despite small sample size. Various AI techniques have been applied in medical research such as artificial neural networks, fuzzy logic, genetic algorithm and other hybrid methods. AI techniques have been proved to generate more accurate predictions than statistical methods and the predictions are based on the individual patient’s conditions as opposed to the statistical methods which made predictions based on a cohort of patients. 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. In order to make a more accurate prognosis, one needs to include both clinicopathologic markers and genomic markers. Currently, there are very few published articles on researches that combine both clinicopathologic and genomic data. Thus, there is a need to use both of the clinicopathologic and genomic markers to improve the accuracy of cancer prognosis. In addition, the mortality rate for oral cancer is high (at approximately 50%) and almost two-thirds of oral cancer occurs in developing countries such as Asian countries, yet there are very few studies using AI techniques in the prognosis of oral cancer. Furthermore, there is no Malaysian study yet on the application of AI techniques in the prognosis of oral cancer. Therefore, there is a need to investigate how AI techniques can be used in the prognosis of oral cancer. The main aim of this research is to apply AI techniques in the prognosis of oral cancer based on the parameters of correlation of clinicopathologic and genomic markers. To this end, a hybrid AI model, namely ReliefF-GA-ANFIS was proposed. The proposed model consists of two stages, where in the first stage, ReliefF-GA is used as feature selection method and in the second stage ANFIS with k-fold cross-validation is used as classifier. The proposed prognostic model was experimented on the oral cancer dataset with optimum feature subsets and validated against three other models which are artificial neural networks, support vector machine and logistic regression. The results for the proposed model of ReliefF-GA-ANFIS outperformed the other three models and the results revealed that the prognosis is superior with the presence of genomic markers. This research provides an insight to apply AI techniques in oral cancer prognosis based on both clinicopathologic and genomic markers. It is hoped that this research is capable of setting a basis for embarking more Malaysians in medical informatics research, particularly in the field of genomic markers.