Cascade learning with abstention and individualised feature selection is a class of models in high demand in personalised medical applications. The cascade consists of sequential classifiers and rejectors , where the classifiers estimate confidence of prediction, and the rejectors evaluate an expected cost-to-go of features not selected yet. The number of models is exponential in the number of features and, therefore, the challenge is to find efficient heuristics for the NP-hard problem. The state-of-the-art is based on complex deep neural networks. We introduce an efficient and robust approach based on a probabilistic graphical model representing a unified probabilistic classifier that can be applied at any stage of a multi-stage sequential model. As for the rejector, we build it on the probabilisticbased neural network that incorporates the very same probabilistic model to treat unobserved feature values. We illustrate the efficiency of the proposed method on several data sets, and compare our results to the state-of-the-art.