Decision support methods in diabetic patient management by insulin administration neural network vs. induction methods for knowledge classification

Ambrosiadou, B, Vadera, S ORCID: https://orcid.org/0000-0001-6041-2646, Shankararaman, V and Goulis, D 2000, Decision support methods in diabetic patient management by insulin administration neural network vs. induction methods for knowledge classification , in: Proc of the ICSC Symposium on Neural Computation, 2000, Berlin, Germany.

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Abstract

Diabetes mellitus is now recognised as a major worldwide public health problem. At present, about 100 million people are registered as diabetic patients. Many clinical, social and economic problems occur as a consequence of insulin-dependent diabetes. Treatment attempts to prevent or delay complications by applying ‘optimal’ glycaemic control. Therefore, there is a continuous need for effective monitoring of the patient. Given the popularity of decision tree learning algorithms as well as neural networks for knowledge classification which is further used for decision support, this paper examines their relative merits by applying one algorithm from each family on a medical problem; that of recommending a particular diabetes regime. For the purposes of this study, OC1 a descendant of Quinlan’s ID3 algorithm was chosen as decision tree learning algorithm and a generating shrinking algorithm for learning arbitrary classifications as a neural network algorithm. These systems were trained on 646 cases derived from two countries in Europe and were tested on 100 cases which were different from the original 646 cases.

Item Type:	Conference or Workshop Item (Paper)
Themes:	Subjects / Themes > Q Science > QA Mathematics > QA075 Electronic computers. Computer science Subjects outside of the University Themes
Schools:	Schools > School of Computing, Science and Engineering Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title:	Proc of the NAISO Symposium on Neural Computing
Publisher:	ICS, Berlin, Germany
Refereed:	Yes
Depositing User:	S Vadera
Date Deposited:	23 Jun 2010 10:07
Last Modified:	15 Feb 2022 17:46
URI:	http://usir.salford.ac.uk/id/eprint/9403

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