A Transferable Belief Model Decision Support Tool over Complementary Clinical Conditions

  • Abderraouf Hadj HenniEmail author
  • David Pasquier
  • Nacim Betrouni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10814)


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.


Belief 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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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Abderraouf Hadj Henni
    • 1
    • 2
    Email author
  • David Pasquier
    • 1
    • 2
  • Nacim Betrouni
    • 3
  1. 1.Cristal UMR CNRS 9189, University of Lille1Villeneuve d’AscqFrance
  2. 2.Department of Radiation OncologyCentre Oscar LambertLilleFrance
  3. 3.INSERM U1171, University of Lille2LilleFrance

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