NDLI: Support vector machines and genetic algorithms for detecting unstable angina

Content Provider	IEEE Xplore Digital Library
Author	Sepulveda-Sanchis, J. Camps-Valls, G. Soria-Olivas, E. Salcedo-Sanz, S. Bousono-Calzon, C. Sanz-Romero, G. Marrugat de la Iglesia, J.
Copyright Year	2002
Description	Author affiliation: Grup de Processament Digital de Senyals, Univ. de Valencia, Spain (Sepulveda-Sanchis, J.; Camps-Valls, G.; Soria-Olivas, E.)
Abstract	We present a combination of two state-of-the-art machine learning methods for predicting mortality in patients with unstable angina (UA). Support vector machines (SVM) are used as non-linear discrimination tools. However, before building the models, selection of the best subset of variables is carried out with genetic algorithms (GA). The best subset of descriptors selected by the GA was constituted by five variables from the originally 75 collected The data was split into a training set (483 patients; 22 cases with UA) and a validation set (243 patients; 12 of cases with UA). The criterion used to select the best model was based on the sensitivity (SE), specificity (SP) and negative predictive values (NPV) in the validation data set. The final SVM model (RBF kernel) yielded good results (SE = 66.67%, SP = 79.77% in the validation set). The recognition rate was 79.12% and a high rate of NPV (97.87%) was obtained. Methods proposed have proven to be well-suited for this problem, simplifying the solution and providing excellent discrimination scores.
Starting Page	413
Ending Page	416
File Size	382903
Page Count	4
File Format	PDF
ISBN	0780377354
ISSN	02766547
DOI	10.1109/CIC.2002.1166797
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-09-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Genetic algorithms Cardiac disease Risk management Hospitals Learning systems Myocardium Ambient intelligence Cardiovascular diseases Medical treatment
Content Type	Text
Resource Type	Article

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