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Acute lymphoblastic leukemia (ALL), the most common cancer in childhood, has its treatment modulated by the risk of relapse. An appropriate estimation of this risk is the most important factor for the definition of treatment strategy. In this paper, we build up a new decision support tool to improve treatment intensity choice in childhood ALL. Our procedure was applied to a significant cohort of Brazilian children with ALL, the majority of the cases treated in the last decade in the two main University Hospitals of Rio de Janeiro. Some intrinsically difficulties of this dataset introduce an assortment of challenges, among those the need of a proper selection of features, clinical and laboratorial data. We apply a mutual information-based methodology for this purpose and a Neural Network to estimate the risk. Among the relapsed patients, 98.2% would have been identified as high-risk by the proposed methodology. The proposed procedure showed significantly better results when compared to the BFM95, a widely used classification protocol.