The paper describes an application of Bayes’ Theorem to the problem of estimating
from past data the probabilities that patients have certain diseases, given their
symptoms. The data consist of hospital records of patients who suffered acute abdominal
pain. For each patient the records showed a large number of symptoms and the final
diagnosis, to one of nine diseases or diagnostic groups. Most current methods of computer
diagnosis use the “Simple Bayes” model in which the symptoms are assumed to be independent,
but the present paper does not make this assumption. Those symptoms (or lack of symptoms)
which are most relevant to the diagnosis of each disease are identified by a sequence
of chi-squared tests. The computer diagnoses obtained as a result of the implementation
of this approach are compared with those given by the “Simple Bayes” method, by the
method of classification trees (CART), and also with the preliminary and final diagnoses
made by physicians.
Key-Words
Bayes’ Theorem - Assumption of Independence - Medical Database - Computer Diagnoses
- Classification Trees
