Abstract:
Acute abdominal pain is one of the most widely studied applications of computer-aided
diagnosis. The usual approach is to apply Bayes’ theorem with the assumption of conditional
independence (“independence Bayes”). We compared various approaches to designing diagnostic
programs for abdominal pain of suspected gynaecological origin. The methods range
from statistical to knowledge-based. All programs were evaluated using a database
of 1,270 cases collected retrospectively. Our results suggest that in this application
no significant improvement in accuracy can be made by taking interactions into account,
either by statistical or by knowledge-based means; independence Bayes is near-optimal.
As far as accuracy is concerned, there appears to be little point in pursuing knowledge-based
approaches. However, the “nearest neighbours” method using a new metric appears to
be at least as accurate as independence Bayes. We argue that the nearest neighbours
method is more suitable than independence Bayes for clinical use because of greater
accountability.
Keywords
Computer-aided Diagnosis - Nearest Neighbours - Abdominal Pain