Abstract:
One of the most accountable methods of providing machine assistance in medical diagnosis
is to retrieve and display similar previously diagnosed cases from a database. In
practice, however, classifying cases according to the diagnoses of their nearest neighbours
is often significantly less accurate than other statistical classifiers. In this paper
the transparency of the nearest neighbours method is combined with the accuracy of
another statistical method. This is achieved by using the other statistical method
to define a measure of similarity between the presentations of two cases. The diagnosis
of abdominal pain of suspected gynaecological origin is used as a case study to evaluate
this method. Bayes’ theorem, with the usual assumption of conditional independence,
is used to define a metric on cases. This new metric was found to correspond as well
as Hamming distance to the clinical notion of “similarity” between cases, while significantly
increasing accuracy to that of the Bayes’ method itself.
Keywords
Computer-Aided Diagnosis - Nearest Neighbours - Database Retrieval - Abdominal Pain
- Explanations
