TY - JOUR A2 - Barreto, Guilherme de Alencar AU - Mena, Luis J. AU - Orozco, Eber E. AU - Felix, Vanessa G. AU - Ostos, Rodolfo AU - Melgarejo, Jesus AU - Maestre, Gladys E. PY - 2012 DA - 2012/08/09 TI - Machine Learning Approach to Extract Diagnostic and Prognostic Thresholds: Application in Prognosis of Cardiovascular Mortality SP - 750151 VL - 2012 AB - Machine learning has become a powerful tool for analysing medical domains, assessing the importance of clinical parameters, and extracting medical knowledge for outcomes research. In this paper, we present a machine learning method for extracting diagnostic and prognostic thresholds, based on a symbolic classification algorithm called REMED. We evaluated the performance of our method by determining new prognostic thresholds for well-known and potential cardiovascular risk factors that are used to support medical decisions in the prognosis of fatal cardiovascular diseases. Our approach predicted 36% of cardiovascular deaths with 80% specificity and 75% general accuracy. The new method provides an innovative approach that might be useful to support decisions about medical diagnoses and prognoses. SN - 1748-670X UR - https://doi.org/10.1155/2012/750151 DO - 10.1155/2012/750151 JF - Computational and Mathematical Methods in Medicine PB - Hindawi Publishing Corporation KW - ER -