Analysing stillbirth data using dynamic self organizing maps Matharage, S, Alahakoon, O, Alahakoon, D, Kapurubandara, S, Nayyar, R, Mukherjee, M, Jagadish, U, Yim, S and Alahakoon, I 2011, Analysing stillbirth data using dynamic self organizing maps, in Proceedings of the 22nd International Workshop on Database and Expert Systems Applications; DEXA 2011, IEEE, Piscataway, NJ, pp. 86-90, doi: 10.1109/DEXA.2011.14. Attached Files Name Description MIMEType Size Downloads Title Analysing stillbirth data using dynamic self organizing maps Author(s) Matharage, S Alahakoon, O Alahakoon, D Kapurubandara, S Nayyar, R Mukherjee, M Jagadish, U Yim, S Alahakoon, I Conference name International Workshop on Database and Expert Systems Applications (22nd : 2011 : Toulouse, France) Conference location Toulouse, France Conference dates 29 Aug. - 2 Sept. 2011 Title of proceedings Proceedings of the 22nd International Workshop on Database and Expert Systems Applications; DEXA 2011 Editor(s) [Unknown] Publication date 2011 Conference series International Workshop on Database and Expert Systems Applications Start page 86 End page 90 Total pages 5 Publisher IEEE Place of publication Piscataway, NJ Keyword(s) Stillbirths Growing Self Organising Map Clustering Perinatal Mortality Data Mining GSOM Summary Even with the presence of modern obstetric care, stillbirth rate seems to stay stagnant or has even risen slightly in countries such as England and has become a significant public health concern [1]. In the light of current medical research, maternal risk factors such as diabetes and hypertensive disease were identified as possible risk factors and are taken into consideration in antenatal care. However, medical practitioners and researchers suspect possible relationships between trends in maternal demographics, antenatal care and pregnancy information of current stillbirth in consideration [2]. Although medical data and knowledge is available appropriate computing techniques to analyze the data may lead to identification of high risk groups. In this paper we use an unsupervised clustering technique called Growing Self organizing Map (GSOM) to analyse the stillbirth data and present patterns which can be important to medical researchers. ISBN 9781457709821 ISSN 1529-4188 Language eng DOI 10.1109/DEXA.2011.14 Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1.1 Full written paper - refereed Copyright notice ©2011, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30060656 Document type: Conference Paper Collection: Faculty of Business and Law Related Links Link Description Connect to pucblished version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 18 Feb 2014, 13:42:42 EST Wed, 19 Feb 2014, 07:22:27 EST Wed, 19 Feb 2014, 07:30:25 EST Wed, 19 Feb 2014, 07:31:50 EST Mon, 14 Apr 2014, 09:27:01 EST Mon, 14 Apr 2014, 13:18:59 EST Mon, 14 Apr 2014, 14:21:44 EST Tue, 15 Apr 2014, 14:56:38 EST Fri, 02 May 2014, 12:47:54 EST Fri, 10 Oct 2014, 09:15:08 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 3 times in Scopus Search Google Scholar Access Statistics: 174 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Tue, 18 Feb 2014, 13:42:26 EST by Sumith Matharage