A survey on computational intelligence approaches for predictive modeling in prostate cancer

Cosma, G ORCID: https://orcid.org/0000-0002-4663-6907, Brown, D ORCID: https://orcid.org/0000-0002-1677-7485, Archer, M, Khan, M and Pockley, AG ORCID: https://orcid.org/0000-0001-9593-6431, 2017. A survey on computational intelligence approaches for predictive modeling in prostate cancer. Expert Systems with Applications, 70, pp. 1-19. ISSN 0957-4174

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Abstract

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex forconventional statistical techniques to process quickly and eciently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes evolutionary algorithms (also known asmetaheuristic optimisation, nature inspired optimisation algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these,as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed.

Item Type:	Journal article
Publication Title:	Expert Systems with Applications
Creators:	Cosma, G., Brown, D., Archer, M., Khan, M. and Pockley, A.G.
Publisher:	Elsevier
Date:	March 2017
Volume:	70
ISSN:	0957-4174
Identifiers:	Number Type 10.1016/j.eswa.2016.11.006 DOI S0957417416306297 Publisher Item Identifier
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	11 Nov 2016 14:47
Last Modified:	09 Nov 2017 03:00
URI:	https://irep.ntu.ac.uk/id/eprint/29077

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