Automated selection of interaction effects in sparse kernel methods to predict pregnancy viability

Support vector machines are highly flexible and generalizing mathematical models that can be used to build prediction models. Their success on a mathematical field is not followed by their application in practice due to their black-box nature. The RBF kernel is often used but the good performance cannot be accompanied by an interpretation of the results. We present a method to visualize the different components of an RBF kernel and propose a method to select the relevant ones. The proposed method is able to automatically detect important main and two-way interaction effects while still obtaining interpretable prediction models. The method is illustrated on a large dataset to predict the viability of pregnancies at the end of the first trimester based on initial scan findings.