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Evolvable Hardware Challenges: Past, Present and the Path to a Promising Future Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Medical Applications of Cartesian Genetic Programming

Authors : Stephen L. Smith, Michael A. Lones

Published in: Inspired by Nature

Publisher: Springer International Publishing

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Abstract

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.

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previous chapter Multi-step Ahead Forecasting Using Cartesian Genetic Programming
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Metadata
Title
Medical Applications of Cartesian Genetic Programming
Authors
Stephen L. Smith
Michael A. Lones
Copyright Year
2018
Book
Inspired by Nature

Print ISBN: 978-3-319-67996-9

Electronic ISBN: 978-3-319-67997-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-67997-6_12

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