2014
DOI: 10.1504/ijcent.2014.065047
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Disease management: clustering-based disease prediction

Abstract: Abstract:The latest trends in healthcare industry have made enormous data available which is useful for disease prediction. This may be utilised by provider/payer for disease management study (DMS) for judging the present health condition and analysing disease trends. The present approach depends upon objective analysis which is based on binary value of symptoms and health indicators and direct relation between them. We propose a framework based on analytics to diagnose and predict diseases using enhanced gene… Show more

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Cited by 6 publications
(3 citation statements)
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References 11 publications
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“…Big data will play a significant role in this transformation [37]. It will allow the information to be delivered to patients directly and empower them to play an active part in their care [5,15,27].…”
Section: Resultsmentioning
confidence: 99%
“…Big data will play a significant role in this transformation [37]. It will allow the information to be delivered to patients directly and empower them to play an active part in their care [5,15,27].…”
Section: Resultsmentioning
confidence: 99%
“…Results showed that k-mean clustering can handle EEG data efficiently in order to detect epileptic seizure. Kaushik et al [170] applied enhanced genetic clustering to diagnose and predict diseases based on patient history, symptoms, and existing medical condition. New patients' profile is analyzed against existing patterns and mapped to a particular cluster based on the patients medical parameters followed by prediction of medical condition and probability of being prone to that disease in future.…”
Section: Clusteringmentioning
confidence: 99%
“…In the 2009 PBC [19], the organization seeks to classify the fiber streamlines of the HDFT dataset into 20-50 fiber tracts consistently within and between brains using unsupervised clustering methods, such as k-means algorithm [23], k-center point algorithm [24] and ISODATA [25]. Following that, in [26], Kulvaibhav et al propose an enhanced genetic clustering method to diagnose and predict diseases using patient's history, symptoms, and existing medical condition. Afterwards, Banu et al [7] utilize Self Organizing Maps based clustering [27] for brain tumor gene expression dataset.…”
Section: Introductionmentioning
confidence: 99%