Analysing Stillbirth Data Using Dynamic Self Organizing Maps

Matharage, Sumith; Alahakoon, Oshadi; Alahakoon, Damminda; Kapurubandara, Supuni; Nayyar, Roshini; Mukherji, Mrityunjoy; Jagadish, U.; Yim, Sang Hyeok; Alahakoon, I.

doi:10.1109/dexa.2011.14

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…The model showed a good accuracy and the inference was made using Mamdani's method. Matharage et al (2011) worked with an unsupervised clustering technique called Growing Self Organizing Map (GSOM) to analyse the stillbirth data and they presented patterns which can be crucial to medical researchers. [6] GSOM is an unsupervised clustering technique which is used to uncover any hidden patterns among stillbirths.…”

Section: Review Of Literaturementioning

confidence: 99%

“…Matharage et al (2011) worked with an unsupervised clustering technique called Growing Self Organizing Map (GSOM) to analyse the stillbirth data and they presented patterns which can be crucial to medical researchers. [6] GSOM is an unsupervised clustering technique which is used to uncover any hidden patterns among stillbirths. A GSOM was trained to identify groupings in maternal demographic data and current stillbirth pregnancy (CSP) data, 6 clusters based on CSP were found and interesting information about the clusters were revealed.…”

Section: Review Of Literaturementioning

confidence: 99%

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Machine Learning for High Risk Pregnancies Pre-Term Birth Prediction: A Retrospective

Ramla¹,

Sangeetha²,

Nickolas³

2018

IJET

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Any birth before 28 weeks of gestation is termed as Pre-Term. This has substantial impact in the emotional reactions of mothers. The post-traumatic stress notably in the mother could be of chronic psychological risk. Moreover, it is to be addressed in the global scenario for sustainable development. Predicting stillbirths is still a distant reality. A plethora of works have been carried out and this paper present the summaries and analysis of current research. The primary focus of the paper is to throw light on the challenging issue of Preterm Birth Prediction. Myriad of machine learning techniques are used by various researchers each with its own estimation accuracy and type of ML model.

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Section: Review Of Literaturementioning

confidence: 99%

Section: Review Of Literaturementioning

confidence: 99%

Machine Learning for High Risk Pregnancies Pre-Term Birth Prediction: A Retrospective

Ramla¹,

Sangeetha²,

Nickolas³

2018

IJET

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“…The Growing Self Organizing Map (GSOM) (Alahakoon et al 2000) is a prominent member of the SOM family and it has shown great potential in different types of clustering tasks. The GSOM algorithm has been used across diverse disciplines due to its capabilities and several applications of the GSOM can be found in Hsu et al (2003), Amarasiri et al (2005), Matharage et al (2009), Ahmad et al (2010), Matharage et al (2011), and Gunasinghe et al (2012).…”

Section: Introductionmentioning

confidence: 99%

Improving the Decision Value of Hierarchical Text Clustering Using Term Overlap Detection

Nathawitharana

Alahakoon

Matharage³

2015

AJIS

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Humans are used to expressing themselves with written language and language provides a medium with which we can describe our experiences in detail incorporating individuality. Even though documents provide a rich source of information, it becomes very difficult to identify, extract, summarize and search when vast amounts of documents are collected especially over time. Document clustering is a technique that has been widely used to group documents based on similarity of content represented by the words used. Once key groups are identified further drill down into sub-groupings is facilitated by the use of hierarchical clustering. Clustering and hierarchical clustering are very useful when applied to numerical and categorical data and cluster accuracy and purity measures exist to evaluate the outcomes of a clustering exercise. Although the same measures have been applied to text clustering, text clusters are based on words or terms which can be repeated across documents associated with different topics. Therefore text data cannot be considered as a direct ‘coding’ of a particular experience or situation in contrast to numerical and categorical data and term overlap is a very common characteristic in text clustering. In this paper we propose a new technique and methodology for term overlap capture from text documents, highlighting the different situations such overlap could signify and discuss why such understanding is important for obtaining value from text clustering. Experiments were conducted using a widely used text document collection where the proposed methodology allowed exploring the term diversity for a given document collection and obtain clusters with minimum term overlap.

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Analysing Stillbirth Data Using Dynamic Self Organizing Maps

Cited by 2 publications

References 6 publications

Machine Learning for High Risk Pregnancies Pre-Term Birth Prediction: A Retrospective

Machine Learning for High Risk Pregnancies Pre-Term Birth Prediction: A Retrospective

Improving the Decision Value of Hierarchical Text Clustering Using Term Overlap Detection

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