Application of Artificial Intelligence-based Technology in Cancer Management: A Commentary on the Deployment of Artificial Neural Networks

Sherbet, Gajanan V., Woo, Wai Lok and Dlay, Satnam (2018) Application of Artificial Intelligence-based Technology in Cancer Management: A Commentary on the Deployment of Artificial Neural Networks. Anticancer Research, 38 (12). pp. 6607-6613. ISSN 0250-7005

Full text not available from this repository.
Official URL: http://dx.doi.org/10.21873/anticanres.13027

Abstract

Artificial intelligence was recognised many years ago as a potential and powerful tool to predict disease outcome in many clinical situations. The conventional approaches using statistical methods have provided much information, but are subject to limitations imposed by the complexity of medical data. The structures of the important variants of the machine learning system artificial neural networks (ANN) are discussed and emphasis is given to the powerful analytical support that could be provided by ANN for the prediction of cancer progression and prognosis. The predictive ability of the cellular markers, DNA ploidy and cell-cycle profiles, and molecular markers, such as tumour promoter and suppressor gene, and growth factor and steroid hormone receptors in breast cancer management were also analysed. ANN systems have been successfully deployed to evaluate microRNA profiles of tumours which saliently sway cancer progression and prognosis of the disease, thus counteracting the negative implications of their numerical abundance. Finally, in this setting, the prospective technical improvements in artificial neural networks, as hybrid systems in combination with fuzzy logic and artificial immune networks were also addressed.

Item Type: Article
Uncontrolled Keywords: Artificial neural networks, binomial logistic regression, breast cancer, DNA ploidy, Fuzzy k-nearest neighbour algorithm, Fuzzy neural networks, growth factor receptors, molecular markers, multilayer perceptron architecture, oestrogen and progesterone receptors statistical analyses, tumour progression and prognosis, tumour promoter and suppressor genes, review
Subjects: B800 Medical Technology
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 25 Mar 2019 13:20
Last Modified: 25 Mar 2019 13:20
URI: http://nrl.northumbria.ac.uk/id/eprint/38531

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