School of Medicine Publications and Presentations
Machine Learning Approach to Extract Diagnostic and Prognostic Thresholds: Application in Prognosis of Cardiovascular Mortality
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.
Mena, L. J., Orozco, E. E., Felix, V. G., Ostos, R., Melgarejo, J., & Maestre, G. E. (2012). Machine Learning Approach to Extract Diagnostic and Prognostic Thresholds: Application in Prognosis of Cardiovascular Mortality. Computational and Mathematical Methods in Medicine, 2012, e750151. https://doi.org/10.1155/2012/750151
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Computational and Mathematical Methods in Medicine
© 2012 Luis J. Mena et al.