Study of Data Mining Techniques Used in Medicinal Services Domain
DOI:
https://doi.org/10.53555/cse.v3i4.160Keywords:
Data Mining, Health Care, Classification,, Clustering,, AssociationAbstract
Medicinal services industry produces a tremendous amount of information that grips complex data identifying with patients and their therapeutic conditions. Data mining is picking up ubiquity in various research fields because of its boundless applications and strategies to mine the data in the right way. Data mining systems have the capacities to find shrouded examples or connections among the articles in the restorative information. In a decade ago, there has been an increment in the utilization of information mining methods on medicinal information for deciding helpful patterns or examples that are utilized as a part of the examination and basic leadership. Information mining has an endless potential to use social insurance information all the more effectively and practically to anticipate diverse sort of infection. This paper highlights different Data mining strategies, for example, order, bunching, and affiliation and furthermore highlights related work to investigate and anticipate human malady.
Downloads
References
C. Hattice & K. Metin, “A DIAGNOSTIC SOFTWARE TOOL FOR SKIN DISEASES WITH BASIC AND WEIGHTED K-NN”, Innovations in Intelligent Systems and Applications (INISTA), 2012.
Dhanya P Varghese & Tintu P B, “A SURVEY ON HEALTH DATA USING DATA MINING TECHNIQUES”, International Research Journal of Engineering and Technology (IRJET), Volume: 02 Issue: 07, Oct- 2015.
Doron Shalvi & Nicholas DeClaris, “AN UNSUPERVISED NEURAL NETWORK APPROACH TO MEDICAL DATA MINING TECHNIQUES”, IEEE, 1998.
Gustavo Santos-Garcia & Gonzalo Varela & Nuria Novoa & Marcelo F. Jimenez, “PREDICTION POSTOPERATIVE MORBIDITY AFTER LUNG RESECTION USING AN ARTIFICIAL NEURAL NETWORK ENSEMBLE”, Artificial Intelligence in Medicine 30:61–69, 2004.
Harleen Kaur & Siri Krishan Wasan, “EMPIRICAL STUDY ON APPLICATIONS OF DATA MINING TECHNIQUES IN HEALTHCARE”, Journal of Computer Science 2 (2): 194-200, 2006.
Hojin Moon & Hongshik Ahn & Ralph Kodell & Songjoon Baek & Chien- Ju Lin & James Chen,“ENSEMBLE METHODS FOR CLASSIFICATION OF PATIENTS FOR PERSONALIZED MEDICINE WITH HIGH-DIMENSIONAL DATA”. Artificial Intelligence in Medicine 41:197–207, 2007.
I. Curiac & G. Vasile & O. Banias & C. Volosencu & A. Albu, “BAYESIAN NETWORK MODEL FOR DIAGNOSIS OF PSYCHIATRIC DISEASES”, Proceedings of the ITI 2009 31st Int. Conf. on Information Technology Interfaces, Cavtat, Croatia, 22-25 June-2009.
Ilayaraja & T. Meyyappan, “MINING MEDICAL DATA TO IDENTIFY FREQUENT DISEASES USING APRIORI ALGORITHM”, Proceedings of the 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering, 21-22 February-2013.
Illhoi Yoo & Patricia Alafaireet & Miroslav Marinov & Keila Pena-Hernandez & Rajitha Gopidi & Jia-Fu Chang & Lei Hua, “DATA MINING IN HEALTHCARE AND BIOMEDICINE: A SURVEY OF THE LITERATURE”, Springer, May-2011.
Jeong-Yon Shim & Lei Xu, “MEDICAL DATA MINING MODEL FOR ORIENTAL MEDICINE VIA BYY BINARY INDEPENDENT FACTOR ANALYSIS”, IEEE.P1-4, 2003.
J.J.Tapia & E. Morett & E. E. Vallejo, “A CLUSTERING GENETIC ALGORITHM FOR GENOMIC DATA MINING”, Foundation[12] J.Yanqing & H.Ying & J.Tran & P.Dews & A.Mansour & R.Michael Massanari, “MINING INFREQUENT CAUSAL ASSOCIATIONS IN ELECTRONIC HEALTH DATABASES”, 11th IEEE International Conference on Data Mining Workshops, 2011.
K.Sharmila & Dr.S.A.Vethamanickam, “SURVEY ON DATA MINING ALGORITHM AND ITS APPLICATION IN HEALTHCARE SECTOR USING HADOOP PLATFORM”, International Journal of Emerging Technology and Advanced Engineering ISSN 2250-2459, Volume: 05, Issue: 01, January-2015.
Yanwei Xing & Jie Wang & Zhihong Zhao & Yonghong Gao, “COMBINATION DATA MINING METHODS WITH NEW MEDICAL DATA TO PREDICTING OUTCOME OF CORNARY.s of Computational Intelligence, Studies in Computational Intelligence, Volume:204, 2009.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2017 gnpublication@
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
In consideration of the journal, Green Publication taking action in reviewing and editing our manuscript, the authors undersigned hereby transfer, assign, or otherwise convey all copyright ownership to the Editorial Office of the Green Publication in the event that such work is published in the journal. Such conveyance covers any product that may derive from the published journal, whether print or electronic. Green Publication shall have the right to register copyright to the Article in its name as claimant, whether separately
or as part of the journal issue or other medium in which the Article is included.
By signing this Agreement, the author(s), and in the case of a Work Made For Hire, the employer, jointly and severally represent and warrant that the Article is original with the author(s) and does not infringe any copyright or violate any other right of any third parties, and that the Article has not been published elsewhere, and is not being considered for publication elsewhere in any form, except as provided herein. Each author’s signature should appear below. The signing author(s) (and, in
