Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 750151, 6 pages
http://dx.doi.org/10.1155/2012/750151
Research Article

Machine Learning Approach to Extract Diagnostic and Prognostic Thresholds: Application in Prognosis of Cardiovascular Mortality

Luis J. Mena,1 Eber E. Orozco,1 Vanessa G. Felix,1 Rodolfo Ostos,1 Jesus Melgarejo,2 and Gladys E. Maestre2,3

1Department of Computer Engineering, Polytechnic University of Sinaloa, 82199 Mazatlan, SIN, Mexico
2Institute for Biological Research and Cardiovascular Institute, Faculty of Medicine, University of Zulia, Maracaibo 4002, Venezuela
3Departments of Psychiatry and Neurology, and the Gertrude H. Sergievsky Center, Columbia University, New York, NY 10032, USA

Received 30 March 2012; Revised 25 June 2012; Accepted 3 July 2012

Academic Editor: Guilherme de Alencar Barreto

Copyright © 2012 Luis J. Mena et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.