Decision tree models for developing molecular classifiers for cancer diagnosis

The aim of this study is to propose a methodology for developing intelligent systems for cancer diagnosis and evaluate it on bladder cancer. Owing to recent advances in high-throughput experiments, large data repositories are now freely available for use. However, the process of extracting information from these data and transforming it into clinically useful knowledge needs to be improved. Consequently, the research focus is shifting from merely data production towards developing methods to manage and analyze it. In this study, we build classification models that are able to discriminate between normal and cancer samples based on the molecular biomarkers discovered. We focus on transparent and interpretable models for data analysis. We built molecular classifiers using decision tree models in combination with boosting and cross-validation to distinguish between normal and malign samples. The approach is designed to avoid overfitting and overoptimistic results. We perform experimental evaluation on a data set related to the urothelial carcinoma of the bladder. We identify a set of tumor microRNAs biomarkers, which integrated in an ensemble of decision tree classifiers, can discriminate between normal and cancer samples with the best published accuracy.