A proposal of a mixed diagnostic system based on decision trees and probabilistic experts rules

Abstract

Decision trees and rule-based expert systems (RBES) are standard diagnostic tools. We propose a mixed technique that starts with a probabilistic decision tree where information is obtained from a real world data base. The decision tree is automatically translated into a set of probabilistic rules. Meanwhile a panel of experts proposes their own set of probabilistic rules, according with their experience on the subject. Both sets of rules are combined, generating a mixed RBES with probabilistic rules. The expected probabilities of the rules translating the knowledge in the decision tree are discretized by considering a mapping from intervals of expected probabilities into a set of five values. This way, knowledge coming from real data is completed with the experience of the panel of experts in order to provide a more accurate prediction of suffering from type 2 diabetes mellitus (T2DM) before seven and a half years in the future. The proposed technique is illustrated with a real case using a diabetes diagnosis probabilistic decision tree built using 1350 out of 1800 real cases and the rules provided by a panel of experts in diabetes. The final result takes into account both the probabilities of the rules and the number of times that each possible consequent is reached, giving a probabilistic result among seven possibilities. For modeling the decision tree, 75% of the individuals in the database (randomly selected) have been used and the rest (25%) have been used to test the results. The results of the Mixed RBES have been compared with the results of the Tree RBES (the RBES built using only the rules from the decision tree) and the results of the Experts’ RBES (the RBES built using only the rules from the panel of experts). The accuracy of the predictions of the Mixed RBES is much better.