The relative accuracy of a variety of medical diagnostic programs.
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.
Todd
B S
BS
Programming Research Group, Oxford University Computing Laboratory, UK.
Stamper
R
R
D015746:Abdominal Pain / Q000209:etiology*; D000293:Adolescent; D000328:Adult; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D016002:Discriminant Analysis; D005260:Female; D005831:Genital Diseases, Female / Q000175:diagnosis*; D006801:Humans; D008875:Middle Aged