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Explaining artificial neural network ensembles: A case study with electrocardiograms from chest pain patients

  • Milos Hauskrecht
Publishing year: 2008
Language: English
Publication/Series: Proceedings of the ICML/UAI/COLT 2008 Workshop on Machine Learning for Health-Care Applications
Document type: Conference paper


Artificial neural networks is one of the most commonly used machine learning algorithms in medical applications. However, they are still not used in practice in the clinics partly due to their lack of explanatory capacity. We compare two case-based explanation methods to two trained physicians on analysis of electrocardiogram (ECG) data from patients with a suspected acute coronary syndrome (ACS). The median overlaps of the top 5 selected features between the two physicians, and a given physician and a method, were initially low. Using a correlation analysis of the features the median overlap increased to values typically in the range 3-4. In conclusion, both our case-based methods generate explanations similar to those of trained expert physicians on the problem of diagnosing ACS from ECG data.


  • Medical and Health Sciences
  • acute coronary syndrome
  • case-based explanation
  • rule extraction
  • neural network ensembles


International Conference on Machine Learning
  • Nuclear medicine, Malmö