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Diagnosing Hyperlipidemia Using Association Rules
Department of Bioengineering, Firat University, Elazig, Turkey
Department of Electronics and Computer Science, Firat University, Elazig, Turkey
Authors to whom correspondence should be addressed.
Published: 1 December 2008
Data mining methodologies have been developed for exploration and analysis of large quantities of data to discover meaningful patterns and rules. This paper presents a new approach, that employs data mining, to find association rules an effective method for discovering Hyperlipidemia. The propose system has been projected from the biochemistry blood parameters which will be very helpful for and will make everything easier for the physicians in the diagnosis of Hyperlipidemia. The basic characteristic of the lipide parameters that is Total cholesterol, LDL, Triglyceride, HDL and VLDL parameters are used in the process of entering the system and finally Hyperlipidemia (T) and Hyperlipidemia (F) results have been evaluated at the end of this process. Data of 492 patients are evaluated in the projected system. The results of the decision support system have completely matched with those of the physicians decisions.
This is an open access article distributed under the Creative Commons Attribution License
(CC BY 3.0).
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MDPI and ACS Style
Dogan, S.; Turkoglu, I. Diagnosing Hyperlipidemia Using Association Rules. Math. Comput. Appl. 2008, 13, 193-202.
Dogan S, Turkoglu I. Diagnosing Hyperlipidemia Using Association Rules. Mathematical and Computational Applications. 2008; 13(3):193-202.
Dogan, Sengul; Turkoglu, Ibrahim. 2008. "Diagnosing Hyperlipidemia Using Association Rules." Math. Comput. Appl. 13, no. 3: 193-202.
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