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

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