Apriori-based Frequent Symptomset Association Mining in Medical Databases
R. P. Ram Kumar1, R. Jayakumar2, A. Sankaridevi3
1R. P. Ram Kumar, Professor, Department of Computer Science and Engineering, Malla Reddy Engineering College Autonomous, Secunderabad (Telangana), India.
2R. Jayakumar, Associate Professor & Head, Department of Computer Applications, Mahendra Engineering College, (Tamil Nadu), India.
3A. Sankaridevi, Assistant Professor, Department of Computer Applications, Mahendra Engineering College, (Tamil Nadu), India.
Manuscript received on 06 February 2019 | Revised Manuscript received on 28 March 2019 | Manuscript Published on 28 April 2019 | PP: 65-68 | Volume-7 Issue-5C February 2019 | Retrieval Number: E10170275C19/19©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Nowadays, healthcare organizations generate large volumes of data. An automatic way of retrieval is necessary when the volume of data is increased. Data mining is becoming very popular and has extensively used in various Healthcare organizations. With the use of various data mining algorithms, it is possible to extract many useful patterns. Data mining applications can highly benefit various parties in Healthcare organization. This paper proposes to enable healthcare organizations by predicting the number of patients affected by certain diseases with respect to their symptoms in medical databases. The pharmacists can use this discovered knowledge and avoid the run out of required drugs, so that the patients can be treated at the right time.
Keywords: Databases Mining Medical Algorithms Organizations Data.
Scope of the Article: Database Theory and Application