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

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Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the ICML/UAI/COLT 2008 Workshop on Machine Learning for Health-Care Applications
EditorsMilos Hauskrecht
Number of pages8
Publication statusPublished - 2008
EventInternational Conference on Machine Learning - Helsinki, Finland
Duration: 2008 Jul 52008 Jul 9

Conference

ConferenceInternational Conference on Machine Learning
Country/TerritoryFinland
CityHelsinki
Period2008/07/052008/07/09

Subject classification (UKÄ)

  • Medical and Health Sciences

Keywords

  • acute coronary syndrome
  • case-based explanation
  • rule extraction
  • neural network ensembles

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