Conceptual framework of a novel hybrid methodology between computational fluid dynamics and data mining techniques for medical dataset application
This thesis proposes a novel hybrid methodology that couples computational fluid dynamic (CFD) and data mining (DM) techniques that is applied to a multi-dimensional medical dataset in order to study potential disease development statistically. This approach allows an alternate solution for the pres...
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| Format: | Thesis |
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Curtin University
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/54143 |
| _version_ | 1848759301049417728 |
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| author | Paramasivam, Vijayajothi |
| author_facet | Paramasivam, Vijayajothi |
| author_sort | Paramasivam, Vijayajothi |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This thesis proposes a novel hybrid methodology that couples computational fluid dynamic (CFD) and data mining (DM) techniques that is applied to a multi-dimensional medical dataset in order to study potential disease development statistically. This approach allows an alternate solution for the present tedious and rigorous CFD methodology being currently adopted to study the influence of geometric parameters on hemodynamics in the human abdominal aortic aneurysm. This approach is seen as a “marriage” between medicine and computer domains. |
| first_indexed | 2025-11-14T09:57:42Z |
| format | Thesis |
| id | curtin-20.500.11937-54143 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:57:42Z |
| publishDate | 2017 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-541432017-07-20T06:16:04Z Conceptual framework of a novel hybrid methodology between computational fluid dynamics and data mining techniques for medical dataset application Paramasivam, Vijayajothi This thesis proposes a novel hybrid methodology that couples computational fluid dynamic (CFD) and data mining (DM) techniques that is applied to a multi-dimensional medical dataset in order to study potential disease development statistically. This approach allows an alternate solution for the present tedious and rigorous CFD methodology being currently adopted to study the influence of geometric parameters on hemodynamics in the human abdominal aortic aneurysm. This approach is seen as a “marriage” between medicine and computer domains. 2017 Thesis http://hdl.handle.net/20.500.11937/54143 Curtin University fulltext |
| spellingShingle | Paramasivam, Vijayajothi Conceptual framework of a novel hybrid methodology between computational fluid dynamics and data mining techniques for medical dataset application |
| title | Conceptual framework of a novel hybrid methodology between computational fluid dynamics and data mining techniques for medical dataset application |
| title_full | Conceptual framework of a novel hybrid methodology between computational fluid dynamics and data mining techniques for medical dataset application |
| title_fullStr | Conceptual framework of a novel hybrid methodology between computational fluid dynamics and data mining techniques for medical dataset application |
| title_full_unstemmed | Conceptual framework of a novel hybrid methodology between computational fluid dynamics and data mining techniques for medical dataset application |
| title_short | Conceptual framework of a novel hybrid methodology between computational fluid dynamics and data mining techniques for medical dataset application |
| title_sort | conceptual framework of a novel hybrid methodology between computational fluid dynamics and data mining techniques for medical dataset application |
| url | http://hdl.handle.net/20.500.11937/54143 |