Random forest: A reliable tool for patient response prediction

The goal of classification is to reliably identify instances that are members of the class of interest. This is especially important for predicting patient response to drugs. However, with high dimensional datasets, classification is both complicated and enhanced by the feature selection process. When designing a classification experiment there are a number of decisions which need to be made in order to maximize performance. These decisions are especially difficult for researchers in fields where data mining is not the focus, such as patient response prediction. It would be easier for such researchers to make these decisions if either their outcomes were chosen or their scope reduced, by using a learner which minimizes the impact of these decisions. We propose that Random Forest, a popular ensemble learner, can serve this role. We performed an experiment involving nineteen different feature selection rankers (eleven of which were proposed and implemented by our research team) to thoroughly test both the Random Forest learner and five other learners. Our research shows that, as long as a large enough number of features are used, the results of using Random Forest are favorable regardless of the choice of feature selection strategy, showing that Random Forest is a suitable choice for patient response prediction researchers who want to do not wish to choose from amongst a myriad of feature selection approaches.