Michael Green, Jonas Björk, Jakob Hansen, Ulf Ekelund, Lars Edenbrandt and Mattias Ohlsson
Detection of acute coronary syndromes in chest pain patients using neural network ensembles

Abstract: Patients with suspicion of acute coronary syndrome (ACS) are difficult to diagnose and they belong to a very heterogenous group of patients. Some require immediate treatment while others, with only minor disorders, may be sent home. Detecting ACS patients using a machine learning approach would be advantageous in many situations. This study is based on patients with chest pain attending the emergency department of Lund University Hospital. A total of 915 cases were incorporated of which 190 were diagnosed as ACS and 725 as non ACS. We have developed classifiers using neural network ensembles that can provide a prediction of ACS for patients with chest pain at an emergency department. We compared two different ensemble strategies, Bagging and K-fold cross splitting. The obtained results were also compared with the results of a standard multiple logistic regression model. Our results show that it is possible to construct a machine learning tool that can predict the presence of ACS among patients with chest pain at a ROC area of 77.8%, corresponding to a level of 40% specificity and 95% sensitivity.

LU TP 05-24