Abstract
Predicting cancer disease state is an important problem in the cancer discovery process. For example, discriminating between benign and malignant tumors improves the medical diagnosis of cancer. Although technological advancing led to the generation of data pertaining to patients with different disease states, evaluating the prediction performance of machine learning algorithms would be an important step. In this paper, we propose using machine learning algorithms such as a variant of AdaBoost, deepboost, xgboost and support vector machines and evaluate them using area under curve and accuracy on real clinical data related to thyroid cancer, colon cancer and liver cancer. Experimental results show the good performance of SVM.