Subspace Discovery for Disease Management : A Case Study in Metabolic Syndrome
This paper identifies key subspaces for better disease management. Disease affects individuals differently based on features such as age, race, and gender. The authors use data mining methods to discover which key factors of a disease are more relevant for particular strata of the population using b...
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| Autor*in: |
Janeja, Vandana P. [verfasserIn] Namayanja, Josephine [author] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2011 |
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Online-Ressource |
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IGI Global InfoSci Journals Archive 2000 - 2012 |
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| Übergeordnetes Werk: |
In: International journal of computational models and algorithms in medicine - Hershey, Pa : IGI Global, 2010, 2(2011), 1, Seite 38-59 |
| Übergeordnetes Werk: |
volume:2 ; year:2011 ; number:1 ; pages:38-59 |
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10.4018/jcmam.2011010103 |
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| 520 | |a This paper identifies key subspaces for better disease management. Disease affects individuals differently based on features such as age, race, and gender. The authors use data mining methods to discover which key factors of a disease are more relevant for particular strata of the population using bin wise clustering. The authors use a case study on Metabolic Syndrome (MetS). MetS is a combination of abnormalities that occur in the body during the processing of food and nutrients. A number of definitions have been studied to classify MetS. No clear criterion exists that can generally fit into a single satisfactory protocol. This domain encompasses a variety of demographics in society, leading to an implication that different criteria may be appropriate for different demographic strata. The authors address this issue and identify the cross section of demographic strata and the disease characteristics that are critical for understanding the disease in that subset of the population. Findings in real world NHANESIII data support this hypothesis, thus the approach can be used by clinical scientists to narrow down specific demographic pools to further study impacts of key MetS characteristics | ||
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10.4018/jcmam.2011010103 doi (DE-627)NLEJ244455120 (VZGNL)10.4018/jcmam.2011010103 DE-627 ger DE-627 rakwb eng Janeja, Vandana P. verfasserin aut Subspace Discovery for Disease Management A Case Study in Metabolic Syndrome 2011 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper identifies key subspaces for better disease management. Disease affects individuals differently based on features such as age, race, and gender. The authors use data mining methods to discover which key factors of a disease are more relevant for particular strata of the population using bin wise clustering. The authors use a case study on Metabolic Syndrome (MetS). MetS is a combination of abnormalities that occur in the body during the processing of food and nutrients. A number of definitions have been studied to classify MetS. No clear criterion exists that can generally fit into a single satisfactory protocol. This domain encompasses a variety of demographics in society, leading to an implication that different criteria may be appropriate for different demographic strata. The authors address this issue and identify the cross section of demographic strata and the disease characteristics that are critical for understanding the disease in that subset of the population. Findings in real world NHANESIII data support this hypothesis, thus the approach can be used by clinical scientists to narrow down specific demographic pools to further study impacts of key MetS characteristics IGI Global InfoSci Journals Archive 2000 - 2012 Clustering K-means Metabolic Syndrome Subspace Discovery Sum of Squared Errors Namayanja, Josephine author aut In International journal of computational models and algorithms in medicine Hershey, Pa : IGI Global, 2010 2(2011), 1, Seite 38-59 Online-Ressource (DE-627)NLEJ244418799 (DE-600)2703045-3 1947-3141 nnns volume:2 year:2011 number:1 pages:38-59 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jcmam.2011010103 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jcmam.2011010103&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 2 2011 1 38-59 |
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10.4018/jcmam.2011010103 doi (DE-627)NLEJ244455120 (VZGNL)10.4018/jcmam.2011010103 DE-627 ger DE-627 rakwb eng Janeja, Vandana P. verfasserin aut Subspace Discovery for Disease Management A Case Study in Metabolic Syndrome 2011 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper identifies key subspaces for better disease management. Disease affects individuals differently based on features such as age, race, and gender. The authors use data mining methods to discover which key factors of a disease are more relevant for particular strata of the population using bin wise clustering. The authors use a case study on Metabolic Syndrome (MetS). MetS is a combination of abnormalities that occur in the body during the processing of food and nutrients. A number of definitions have been studied to classify MetS. No clear criterion exists that can generally fit into a single satisfactory protocol. This domain encompasses a variety of demographics in society, leading to an implication that different criteria may be appropriate for different demographic strata. The authors address this issue and identify the cross section of demographic strata and the disease characteristics that are critical for understanding the disease in that subset of the population. Findings in real world NHANESIII data support this hypothesis, thus the approach can be used by clinical scientists to narrow down specific demographic pools to further study impacts of key MetS characteristics IGI Global InfoSci Journals Archive 2000 - 2012 Clustering K-means Metabolic Syndrome Subspace Discovery Sum of Squared Errors Namayanja, Josephine author aut In International journal of computational models and algorithms in medicine Hershey, Pa : IGI Global, 2010 2(2011), 1, Seite 38-59 Online-Ressource (DE-627)NLEJ244418799 (DE-600)2703045-3 1947-3141 nnns volume:2 year:2011 number:1 pages:38-59 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jcmam.2011010103 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jcmam.2011010103&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 2 2011 1 38-59 |
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10.4018/jcmam.2011010103 doi (DE-627)NLEJ244455120 (VZGNL)10.4018/jcmam.2011010103 DE-627 ger DE-627 rakwb eng Janeja, Vandana P. verfasserin aut Subspace Discovery for Disease Management A Case Study in Metabolic Syndrome 2011 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper identifies key subspaces for better disease management. Disease affects individuals differently based on features such as age, race, and gender. The authors use data mining methods to discover which key factors of a disease are more relevant for particular strata of the population using bin wise clustering. The authors use a case study on Metabolic Syndrome (MetS). MetS is a combination of abnormalities that occur in the body during the processing of food and nutrients. A number of definitions have been studied to classify MetS. No clear criterion exists that can generally fit into a single satisfactory protocol. This domain encompasses a variety of demographics in society, leading to an implication that different criteria may be appropriate for different demographic strata. The authors address this issue and identify the cross section of demographic strata and the disease characteristics that are critical for understanding the disease in that subset of the population. Findings in real world NHANESIII data support this hypothesis, thus the approach can be used by clinical scientists to narrow down specific demographic pools to further study impacts of key MetS characteristics IGI Global InfoSci Journals Archive 2000 - 2012 Clustering K-means Metabolic Syndrome Subspace Discovery Sum of Squared Errors Namayanja, Josephine author aut In International journal of computational models and algorithms in medicine Hershey, Pa : IGI Global, 2010 2(2011), 1, Seite 38-59 Online-Ressource (DE-627)NLEJ244418799 (DE-600)2703045-3 1947-3141 nnns volume:2 year:2011 number:1 pages:38-59 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jcmam.2011010103 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jcmam.2011010103&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 2 2011 1 38-59 |
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This paper identifies key subspaces for better disease management. Disease affects individuals differently based on features such as age, race, and gender. The authors use data mining methods to discover which key factors of a disease are more relevant for particular strata of the population using bin wise clustering. The authors use a case study on Metabolic Syndrome (MetS). MetS is a combination of abnormalities that occur in the body during the processing of food and nutrients. A number of definitions have been studied to classify MetS. No clear criterion exists that can generally fit into a single satisfactory protocol. This domain encompasses a variety of demographics in society, leading to an implication that different criteria may be appropriate for different demographic strata. The authors address this issue and identify the cross section of demographic strata and the disease characteristics that are critical for understanding the disease in that subset of the population. Findings in real world NHANESIII data support this hypothesis, thus the approach can be used by clinical scientists to narrow down specific demographic pools to further study impacts of key MetS characteristics |
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This paper identifies key subspaces for better disease management. Disease affects individuals differently based on features such as age, race, and gender. The authors use data mining methods to discover which key factors of a disease are more relevant for particular strata of the population using bin wise clustering. The authors use a case study on Metabolic Syndrome (MetS). MetS is a combination of abnormalities that occur in the body during the processing of food and nutrients. A number of definitions have been studied to classify MetS. No clear criterion exists that can generally fit into a single satisfactory protocol. This domain encompasses a variety of demographics in society, leading to an implication that different criteria may be appropriate for different demographic strata. The authors address this issue and identify the cross section of demographic strata and the disease characteristics that are critical for understanding the disease in that subset of the population. Findings in real world NHANESIII data support this hypothesis, thus the approach can be used by clinical scientists to narrow down specific demographic pools to further study impacts of key MetS characteristics |
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This paper identifies key subspaces for better disease management. Disease affects individuals differently based on features such as age, race, and gender. The authors use data mining methods to discover which key factors of a disease are more relevant for particular strata of the population using bin wise clustering. The authors use a case study on Metabolic Syndrome (MetS). MetS is a combination of abnormalities that occur in the body during the processing of food and nutrients. A number of definitions have been studied to classify MetS. No clear criterion exists that can generally fit into a single satisfactory protocol. This domain encompasses a variety of demographics in society, leading to an implication that different criteria may be appropriate for different demographic strata. The authors address this issue and identify the cross section of demographic strata and the disease characteristics that are critical for understanding the disease in that subset of the population. Findings in real world NHANESIII data support this hypothesis, thus the approach can be used by clinical scientists to narrow down specific demographic pools to further study impacts of key MetS characteristics |
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