A hybrid Decision Support System for the risk assessment of retinopathy development as a long term complication of Type 1 Diabetes Mellitus

Skevofilakas, M; Zarkogianni, K; Karamanos, BG; Nikita, KS

dc.contributor.author	Skevofilakas, M	en
dc.contributor.author	Zarkogianni, K	en
dc.contributor.author	Karamanos, BG	en
dc.contributor.author	Nikita, KS	en
dc.date.accessioned	2014-03-01T02:46:36Z
dc.date.available	2014-03-01T02:46:36Z
dc.date.issued	2010	en
dc.identifier.issn	1557170X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32748
dc.subject	Classification and Regression Tree	en
dc.subject	Design and Development	en
dc.subject	Electronic Medical Record	en
dc.subject	Information Technology	en
dc.subject	Risk Assessment	en
dc.subject	Rule Induction	en
dc.subject	Support System	en
dc.subject	type 1 diabetes mellitus	en
dc.subject	Close Coupling	en
dc.subject	Decision Support System	en
dc.subject	Feedforward Neural Network	en
dc.subject	Wavelet Neural Network	en
dc.subject.other	Classification and regression tree	en
dc.subject.other	Classification models	en
dc.subject.other	Clinical state	en
dc.subject.other	Data repositories	en
dc.subject.other	Electronic medical record	en
dc.subject.other	Excellent performance	en
dc.subject.other	Information technologies (it)	en
dc.subject.other	Long term	en
dc.subject.other	Possible futures	en
dc.subject.other	Research teams	en
dc.subject.other	Rule induction	en
dc.subject.other	Smart agents	en
dc.subject.other	Type 1 diabetes mellitus	en
dc.subject.other	Voting mechanism	en
dc.subject.other	Wavelet neural networks	en
dc.subject.other	Wealth of information	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Decision making	en
dc.subject.other	Decision support systems	en
dc.subject.other	Decision theory	en
dc.subject.other	Eye protection	en
dc.subject.other	Feedforward neural networks	en
dc.subject.other	Health risks	en
dc.subject.other	Medical computing	en
dc.subject.other	Medicine	en
dc.subject.other	Risk assessment	en
dc.title	A hybrid Decision Support System for the risk assessment of retinopathy development as a long term complication of Type 1 Diabetes Mellitus	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IEMBS.2010.5626245	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IEMBS.2010.5626245	en
heal.identifier.secondary	5626245	en
heal.publicationDate	2010	en
heal.abstract	The aim of the present study is to design and develop a Decision Support System (DSS) closely coupled with an Electronic Medical Record (EMR), able to predict the risk of a Type 1 Diabetes Mellitus (T1DM) patient to develop retinopathy. The proposed system is able to store a wealth of information regarding the clinical state of the T1DM patient and continuously provide the health experts with predictions regarding the possible future complications that he may present. The DSS is a hybrid infrastructure combining a Feedforward Neural Network (FNN), a Classification and Regression Tree (CART) and a Rule Induction C5.0 classifier, with an improved Hybrid Wavelet Neural Network (iHWNN). A voting mechanism is utilized to merge the results from the four classification models. The proposed DSS has been trained and evaluated using data from 55 T1DM patients, acquired by the Athens Hippokration Hospital in close collaboration with the EURODIAB research team. The DSS has shown an excellent performance resulting in an accuracy of 98%. Care has been taken to design and implement a consistent and continuously evolving Information Technology (IT) system by utilizing technologies such as smart agents periodically triggered to retrain the DSS with new cases added in the data repository. © 2010 IEEE.	en
heal.journalName	2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10	en
dc.identifier.doi	10.1109/IEMBS.2010.5626245	en
dc.identifier.volume	2010	en
dc.identifier.spage	6713	en
dc.identifier.epage	6716	en