Abstract

SPROCKEL, John J; DIAZTAGLE, Juan J; ALZATE, Wilson  and  GONZALEZ, Enrique. Neural networks for the diagnosis of acute myocardial infarction. Rev. Colomb. Cardiol. [online]. 2014, vol.21, n.4, pp.215-223. ISSN 0120-5633.  https://doi.org/10.1016/j.rccar.2013.10.001.

Introduction; Myocardial infarction represents the leading cause of death by a noncommunicable disease worldwide; one of the tools that serve as decision support for establishing a diagnosis are neural networks. They have been shown to have a good level of accuracy. Methods; Training and testing of several neural networks was performed with different architectures for the diagnosis of the myocardial infarction in a group of patients admitted with chest pain emergency room in the Hospital de San José, Bogotá. This was carried out according to data from the incidence scale of Braunwald's classification of unstable angina. Results; Forty networks were generated and tested in five experiments obtaining an accurate diagnostic with the electrocardiographic pattern of five entries and troponin. The negative predictive value was 100% in the model with ten clinical variables, electrocardiogram and troponin. Some of the designed networks had a sensitivity and specificity of 100%. A validation study to verify these findings is required. Conclusions; With the results found for neural networks in the literature and in the present study, we should consider the practical use of this computational intelligence strategy in daily practice.

Keywords : Chest pain; Coronary disease; Myocardial acute infarction; Electrocardiogram.

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