A Transferable Belief Model Decision Support Tool over Complementary Clinical Conditions
This paper presents an algorithm for decision support over two complementary clinical conditions given a large features data base. The algorithm is mainly divided in two parts, the first one aims at identifying relevant features from a large dimension data base using a heuristic method based on a discriminating power. The second part is a tool based on the Transferable Belief Model (TBM) which combines information extracted from the selected features to provide decision results with probabilities along with a result’s consistency measure so that decision could be made carefully. The proposed algorithm is tested on a downloaded feature data base. The TBM based decision support tool showed consistent results w.r.t provided outcomes by combining data from two relevant features identified after using the heuristic feature ranking method.
KeywordsBelief functions Transferable Belief Model Decision support Bio marker identification Data fusion
First author would like to thank professor O. COLOT for his basic belief functions courses given at the university of Lille 1. Work of this paper has been funded by COL (Centre Oscart Lambret, Lille-France) during an internship in 2016.
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