DocumentCode :
2001408
Title :
Descriptive decision-making of lung cancer treatment using a DM model with Dempster-Shafer theory and Prospect theory
Author :
Nusrat, E. ; Yamda, K.
Author_Institution :
Dept. of Manage. & Inf. Syst. Sci., Nagaoka Univ. of Technol., Nagaoka, Japan
fYear :
2012
fDate :
20-24 Nov. 2012
Firstpage :
359
Lastpage :
364
Abstract :
This paper explains a descriptive decision-making framework under uncertainty elucidating different decision attitudes of a newly-diagnosed lung cancer patient regarding treatment decision. The problem of treatment decision making of a lung cancer patient has been defined as Evidential Decision-making Problem (EDMP) at first. Belief function of Dempster-Shafer theory is exploited to explain uncertainty of EDMP and Prospect theory is applied to accomplish a descriptive decision-making framework. The goal of this paper is to find what decisions regarding treatments are made by a lung cancer victim when attitudes towards uncertainty are different.
Keywords :
belief networks; decision making; inference mechanisms; medical computing; uncertainty handling; DM model; Dempster-Shafer theory; EDMP; belief function; decision attitudes; descriptive decision-making framework; evidential decision-making problem; lung cancer treatment; newly-diagnosed lung cancer patient; prospect theory; Dempster-Shafer Theory; Prospect theory; decision attitude; decision-making; lung cancer; uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012 Joint 6th International Conference on
Conference_Location :
Kobe
Print_ISBN :
978-1-4673-2742-8
Type :
conf
DOI :
10.1109/SCIS-ISIS.2012.6505031
Filename :
6505031
Link To Document :
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