Validation of a Federation of Collaborative Rational Agents for the Diagnosis of Acute Coronary Syndromes in a Population with High Probability

Acute myocardial infarction is the main cause of death worldwide, it is part of the acute coronary syndromes (ACS) which are characterized by an acute obstruction of the blood flow in the arteries of the heart. ACS diagnosis poses a highly complex problem where the use of intelligent systems represents an opportunity for the optimization of the diagnosis. The objective of the present work is to perform a cross validation of a federation of collaborative rational agents for the diagnosis of ACS in a population with high probability exhibiting chest pain. A study of diagnostic tests was performed, the diagnostic standard criterion was the third redefinition of infarction or some strategy for coronary stratification. The index test was the result of a system based on a federation of collaborative rational agents based on the assembly of neural networks by means of a weighted voting system in accordance with positive likelihood ratios. A sample of 108 patients was calculated and a contingency table was built in order to calculate the operational characteristics. 148 patients were taken into consideration, ACS was discarded in 29,2%, 51,7 exhibited acute infarction, and 19,1% exhibited unstable angina. The federation system reached a precision of 79%, sensibility of 97,1%, specificity of 36,4%, and AUC of 0,672. It is concluded that a multi-agent system based on the assembly of neural networks attained an acceptable performance for the diagnosis of ACS in a population with high probability.