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A Data-Mining Approach for Investigating Social and Economic Geographical Dynamics of beta-Thalassemia's Spread

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dc.creator Pogue, Brian W.
dc.creator Akay, Altug
dc.creator Yesilipek, Akif
dc.creator YARDIMCI, AHMET
dc.creator Canatan, Duran
dc.creator Dragomir, Andrei
dc.date 2009-09-01T00:00:00Z
dc.date.accessioned 2021-12-03T12:03:42Z
dc.date.available 2021-12-03T12:03:42Z
dc.identifier dc51581a-d894-4a25-a850-094d7936cfdc
dc.identifier 10.1109/titb.2009.2020062
dc.identifier https://avesis.sdu.edu.tr/publication/details/dc51581a-d894-4a25-a850-094d7936cfdc/oai
dc.identifier.uri http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/95297
dc.description beta-Thalassemia is an anemic genetic disorder that remains a major global health issue, especially in the globalized era where public health, economics, and education are tightly interwoven. Previous studies have examined the disease's rate and heredity. This study analyzed beta-thalassemia's socioeconomic geography and how it affects the afflicted population. We processed survey data and performed data mining using self-organizing maps to identify underlying data structure. We hypothesized that certain variables mark subgroups within the affected population and we aimed at identifying these subgroups and used a correlation-based measure to assess the variable's importance to the subgroup's distinction. The population's education level was one of the major factors that divided it into different subgroups. Our study showed that recurring patterns of specific variables separated the affected population into disparate subgroups based on their response to questionnaires. Future studies can use such tools to delve deeper into how other variables (e.g. socioeconomic and genomic) can identify subgroups within larger affected populations.
dc.language eng
dc.rights info:eu-repo/semantics/closedAccess
dc.title A Data-Mining Approach for Investigating Social and Economic Geographical Dynamics of beta-Thalassemia's Spread
dc.type info:eu-repo/semantics/article


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