Ensemble decision trees to predict if a profiled bystander can use an AED to deliver a shock

Raymond Bond, Peter O’Hare, Hannah Torney, Laura Davis, Bruno Delafont, Hannah McReynolds, Anna McLister, Ben McCartney, Rebecca Di Maio, Dewar Finlay, Daniel Guldenring, James McLaughlin, David McEneaney

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


BackgroundEach year cardiac arrest kills 60,000 people in the UK [1-2]. The use of an automatic external defibrillator (AED) in the first few minutes can increase the probability of survival from less than 5% to over 75%. However, there is a challenge to build AEDs that are ‘usable’ to all members of the public. This study investigated if we could predict whether a profiled bystander is likely to succeed in delivering a shock. MethodsA total of 362 subjects were recruited at a shopping mall and were asked to use an AED in a simulated emergency situation as facilitated by a ‘sensorised’ mannequin. During this we extracted a range of features (i.e. Age, Gender, Education, CPR/AED Training, Time-to-Place-Electrodes, Time-to-First-Shock). Using logistic regression, Odds ratios (ORs) were analysed to identify variables that decrease/increase the likelihood of delivering a shock. The dataset was also split into training (70%) and testing datasets (30%) to build and evaluate an ensemble C5.0 decision tree to predict if a person is likely to deliver a successful shock. ResultsThe variables (1) Time-to-First-Shock [OR= 8.97], (2) Prior Defibrillation Training [OR= 8.37], (3) Prior CPR Training [OR= 6.76], High school education [OR= 5.71]) have the largest ORs. This may indicate that users with these characteristics are likely to deliver a shock. However, no OR had statistical significance (p
Original languageEnglish
Title of host publicationUnknown Host Publication
Number of pages1
Publication statusPublished (in print/issue) - 27 Oct 2015
Event7th Annual Translational Medicine Conference - Derry/Londonderry
Duration: 27 Oct 2015 → …


Conference7th Annual Translational Medicine Conference
Period27/10/15 → …


  • machine learning
  • health informatics
  • human computer interaction


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