Michael Green, Ulf Ekelund, Lars Edenbrandt, Jonas Björk, Jakob L. Foreberg and Mattias Ohlsson
Explaining artificial neural network ensembles: A case study with electrocardiograms from chest pain patients
Proceedings of the 25th International Conference on Machine Learning (2008)
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
2-3. In conclusion, both our case-based methods
generate explanations somewhat similar to those of
trained expert physicians on the problem of
diagnosing ACS from ECG data.
LU TP 08-06