Bayesian classification with local probabilistic model assumption in aiding medical diagnosis

In computer-aided diagnosis, a Bayesian classifier that can give the class membership probabilities should be more favorable than classifiers that only give a class assertion. In Bayesian classification, an important and critical step is the probability distribution estimation for each class. Existing methods usually estimate the probability distribution in the whole sample space where the original distribution may be too complex to model. In this paper, we propose a probability distribution estimation method based on local probabilistic model assumption. In our method, the estimation of global probability for a certain point is transformed to the computation of local distribution in a small region, where the local distribution is supposed to be simpler and can be assumed as a simpler probabilistic model. By this method, we implement the Bayesian classifiers based on several local probabilistic model assumptions, and experiments with these classifier have been conducted on several real-word biological and medical datasets; the experimental results demonstrate the efficacy of the proposed method for probabilistic classification in medical diagnosis.