Support vector machines and genetic algorithms for detecting unstable angina

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.