Using machine learning to predict if a profiled lay rescuer can successfully deliver a shock using a public access automated external defibrillator?

A public access automated external defibrillator (AED) is a device that is intended to be used by lay rescuers in an event where a member of the public experiences a sudden cardiac arrest due to a severe ventricular arrhythmia. Therefore, it is imperative that the human-machine interface of an AED is optimized in terms of its usability and intuitive design. This study involved the recruitment of362 subjects (lay people) in a shopping mall to undertake the task of using an AED in a simulated environment as facilitated by a `sensorised' manikin and an AED that was developed by HeartSine Technologies. We found that a large proportion (91.44%) of lay people can successfully use an AED in a simulated emergency scenario to deliver a successful shock. We also found that CPR training did not provide greater likelihood for shock success whilst those with AED training did. Exploratory data analysis and machine learning were used to determine if demographics and other variables are potential predictors for delivering a successful shock using an AED. We found that user demographics and educational attainment were not predictive for AED `usage' success, which is reassuring since the objective of the medical industry is to develop AEDs that are intuitive to any member of the public.