Blood clotting prediction model using Artificial Neural Networks and Sensor Networks

Mirtskhulava, Lela, Al-Majeed, Salah ORCID: 0000-0002-5932-9658, Pearce, Gillian, Gogoladze, Tamar and Javakhishvili, Ivane (2014) Blood clotting prediction model using Artificial Neural Networks and Sensor Networks. Computer Science and Telecommunications, 3 (43). pp. 60-66.

Text (Published version) 6013 Al-Majeed (2014) Blood clotting prediction model using Artificial Neural Networks and Sensor Networks.pdf - Published Version Restricted to Repository staff only until 1 January 2099. (Other reason). Available under License All Rights Reserved. Download (163kB)

Abstract

The purpose of the given paper is to analyze blood clots (BCs) by using Artificial Neural Network (ANN) using the physical symptoms as the input data. Such a NN provides an analytical alternative to conventional techniques, and allows the user to model BCs. Sensor Data application provides direct access to data. All the data have been collected by using different types of sensors. Based on the symptoms these sensors can pass the data (offline or in real time) to the NN, where the latter will analyze it though a modelling system designed to distinguish the blood clotting. This paper illustrates the effect of using a combination of different types of sensors. These sensors will provide inputs to a well-designed NN that aims to model the BC, and analyse it in a way that gives better predictions of the presence of a BC or at least an early warning indicating BC presence. By using this model, developed in the given paper, the patients will be able to predefine danger of occurrence of blood clotting.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Artificial neural network model; Blood clotting prediction; Sensor measurement
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas:	Applied Business & Technology
Depositing User:	Susan Turner
Date Deposited:	21 Sep 2018 08:42
Last Modified:	31 Aug 2023 08:01
URI:	https://eprints.glos.ac.uk/id/eprint/6013

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