Applied multivariate statistical analysis
Applied multivariate statistical analysis
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Elements of information theory
Elements of information theory
Neural Computation
A practical Bayesian framework for backpropagation networks
Neural Computation
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Beyond second-order statistics for learning: A pairwise interaction model for entropy estimation
Natural Computing: an international journal
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Neural-network feature selector
IEEE Transactions on Neural Networks
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
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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.