Intelligent analysis in predicting outcome of out-of-hospital cardiac arrest.

The prognosis among patients who suffer out-of-hospital cardiac arrest is poor. Higher survival rates have been observed only in patients with ventricular fibrillation who were fortunate enough to have basic and advanced life support initiated early after cardiac arrest. The ability to predict outcomes of cardiac arrest would be useful for resuscitation chains. Levels of EtCO(2)in expired air from lungs during cardiopulmonary resuscitation may serve as a non-invasive predictor of successful resuscitation and survival from cardiac arrest. Six different supervised learning classification techniques were used and evaluated. It has been shown that machine learning methods can provide an efficient way to detect important prognostic factors upon which further emergency unit actions are based.