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2011 22nd International Workshop on Database and Expert Systems Applications
Analysing Stillbirth Data Using Dynamic Self Organizing Maps
Toulouse, France
August 29-September 02
ISBN: 978-0-7695-4486-1
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.
Index Terms:
Stillbirths, Clustering, Perinatal Mortality, Data Mining, GSOM
Citation:
S. Matharage, O. Alahakoon, D. Alahakoon, S. Kapurubandara, R. Nayyar, M. Mukherji, U. Jagadish, S. Yim, I. Alahakoon, "Analysing Stillbirth Data Using Dynamic Self Organizing Maps," dexa, pp.86-90, 2011 22nd International Workshop on Database and Expert Systems Applications, 2011
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