Artificial Neural Network Model in Stroke Diagnosis

Mirtskhulava, Lela, Wong, Julian, Al-Majeed, Salah ORCID: 0000-0002-5932-9658 and Pearce, Gillian (2016) Artificial Neural Network Model in Stroke Diagnosis. In: 2015 17th UKSim-AMSS International Conference on Modelling and Simulation, 25-27 March, Cambridge, UK.

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Abstract

We present a model of Artificial Neural Network (ANN) applied to stroke diagnosis. We use input data related to stroke that serves as inputs for the ANN. These data include clinical symptoms together with stroke risk factors. Each type of data provides input that is evaluated and used during the ANN processing. The adaptive learning algorithm can be used with a plethora of types of medical data and integrated into categorized outputs.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Computational modelling; Computer science; Surgery; Medical diagnosis; Europe
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas:	Applied Business & Technology
Depositing User:	Susan Turner
Date Deposited:	20 Apr 2018 16:04
Last Modified:	31 Aug 2023 08:01
URI:	https://eprints.glos.ac.uk/id/eprint/5581

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