NDLI: Multipopulation genetic programming applied to burn diagnosing

Content Provider	IEEE Xplore Digital Library
Author	Fernandez de Vega, F. Roa, L.M. Tomassini, M. Sanchez, J.M.
Copyright Year	2000
Description	Author affiliation: Dipt. Inf., Univ. de Extremadura, Caceres, Spain (Fernandez de Vega, F.)
Abstract	Genetic programming (GP) has proved useful in optimization problems. The way of representing individuals in this methodology is particularly good when we want to construct decision trees. Decision trees are well suited to representing explicit information and relationships among parameters studied. A set of decision trees could make up a decision support system. In this paper we set out a methodology for developing decision support systems as an aid to medical decision making. Above all, we apply it to diagnosing the evolution of a burn, which is a really difficult task even for specialists. A learning classifier system is developed by means of multipopulation genetic programming (MGP). It uses a set of parameters, obtained by specialist doctors, to predict the evolution of a burn according to its initial stages. The system is first trained with a set of parameters and results of evolutions which have been recorded over a set of clinic cases. Once the system is trained, it is useful for deciding how new cases will probably evolve. Thanks to the use of GP, an explicit expression of the input parameter is provided. This explicit expression takes the form of a decision tree which will be incorporated into software tools that help physicians In their everyday work.
Starting Page	1292
Ending Page	1296
File Size	438063
Page Count	5
File Format	PDF
ISBN	0780363752
DOI	10.1109/CEC.2000.870800
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-07-16
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic programming Decision trees Medical diagnostic imaging Decision support systems Classification tree analysis Software tools Knowledge based systems Data mining Decision making Tissue damage
Content Type	Text
Resource Type	Article

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