Medical Applications of Cartesian Genetic Programming

Stephen L. Smith, Michael Adam Lones

Research output: Chapter in Book/Report/Conference proceedingChapter


The application of machine learning techniques to problems in medicine are now becoming widespread, but the rational and advantages of using a particular approach is not always clear or justified. This chapter describes the application of a version of Cartesian Genetic Programming (CGP), termed Implicit Context Representation CGP, to two very different medical applications: diagnosis and monitoring of Parkinson’s disease, and the differential diagnosis of thyroid cancer. Importantly, the use of CGP brings two major benefits: one is the generation of high performing classifiers, and the second, an understanding of how the patient measurements are used to form these classifiers. The latter is typically difficult to achieve using alternative machine learning methods and also provides a unique understanding of the underlying clinical conditions.
Original languageEnglish
Title of host publicationInspired by Nature
EditorsSusan Stepney, Andrew Adamatzky
Number of pages20
ISBN (Electronic)9783319679976
ISBN (Print)9783319679969
Publication statusPublished - 2018

Publication series

NameEmergence, Complexity and Computation
ISSN (Print)2194-7287


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