Data mining approaches for kidney dialysis treatment
Data mining techniques has been used as a recent trend for achieving diagnostics results, especially in medical fields such as kidney dialysis, skin cancer and breast cancer detection, and also biological sequences classification. Due to its ability to discover the relationship and pattern of the me...
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| Format: | Article |
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2006
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| Online Access: | http://shdl.mmu.edu.my/1965/ |
| _version_ | 1848789926376636416 |
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| author | SRIRAAM, N. |
| author_facet | SRIRAAM, N. |
| author_sort | SRIRAAM, N. |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | Data mining techniques has been used as a recent trend for achieving diagnostics results, especially in medical fields such as kidney dialysis, skin cancer and breast cancer detection, and also biological sequences classification. Due to its ability to discover the relationship and pattern of the medical database, early detection or prediction of pathological conditions through mining has become feasible. This paper discusses the data mining approach for parametric evaluation to improve the treatment of kidney dialysis patient. The experimental result shows that classification accuracy using Association mining between the ranges 50-97.7% is obtained based on the dialysis parameter combination. Such a decision-based approach helps the clinician to decide the level of dialysis required for individual patient. |
| first_indexed | 2025-11-14T18:04:29Z |
| format | Article |
| id | mmu-1965 |
| institution | Multimedia University |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:04:29Z |
| publishDate | 2006 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-19652011-09-23T03:22:27Z http://shdl.mmu.edu.my/1965/ Data mining approaches for kidney dialysis treatment SRIRAAM, N. QC Physics Data mining techniques has been used as a recent trend for achieving diagnostics results, especially in medical fields such as kidney dialysis, skin cancer and breast cancer detection, and also biological sequences classification. Due to its ability to discover the relationship and pattern of the medical database, early detection or prediction of pathological conditions through mining has become feasible. This paper discusses the data mining approach for parametric evaluation to improve the treatment of kidney dialysis patient. The experimental result shows that classification accuracy using Association mining between the ranges 50-97.7% is obtained based on the dialysis parameter combination. Such a decision-based approach helps the clinician to decide the level of dialysis required for individual patient. 2006-06 Article NonPeerReviewed SRIRAAM, N. (2006) Data mining approaches for kidney dialysis treatment. Journal of Mechanics in Medicine and Biology, 6 (2). pp. 109-121. ISSN 02195194 http://dx.doi.org/10.1142/S0219519406001893 doi:10.1142/S0219519406001893 doi:10.1142/S0219519406001893 |
| spellingShingle | QC Physics SRIRAAM, N. Data mining approaches for kidney dialysis treatment |
| title | Data mining approaches for kidney dialysis treatment |
| title_full | Data mining approaches for kidney dialysis treatment |
| title_fullStr | Data mining approaches for kidney dialysis treatment |
| title_full_unstemmed | Data mining approaches for kidney dialysis treatment |
| title_short | Data mining approaches for kidney dialysis treatment |
| title_sort | data mining approaches for kidney dialysis treatment |
| topic | QC Physics |
| url | http://shdl.mmu.edu.my/1965/ http://shdl.mmu.edu.my/1965/ http://shdl.mmu.edu.my/1965/ |