The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
A survey on Data Mining approaches for Healthcare
Open accessAbstract
Data Mining is one of the most motivating area of research that is become increasingly popular in health organization.Data Mining plays an important role for uncovering new trends in healthcare organization which in turn helpful for all the parties associated with this field.This survey explores the utility of various Data Mining techniques such as classification, clustering, association, regression in health domain.In this paper, we present a brief introduction of these techniques and their advantages and disadvantages.This survey also highlights applications, challenges and future issues of Data Mining in healthcare.Recommendation regarding the suitable choice of available Data Mining technique is also discussed in this paper.
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Cites
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
The KDD process for extracting useful knowledge from volumes of data
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996) · Communications of the ACM · doi:10.1145/240455.240464 · Cited by 1,934
Predictive data mining in clinical medicine: Current issues and guidelines
BELLAZZI, R., & ZUPAN, B. (2008) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2006.11.006 · Cited by 823
Effective diagnosis of heart disease through neural networks ensembles
Das, R., Turkoglu, I., & Sengur, A. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.09.013 · Cited by 628
Association rule mining to detect factors which contribute to heart disease in males and females
Nahar, J., Imam, T., Tickle, K. S., & Chen, Y.-P. P. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.08.028 · Cited by 394
Cited by
Aceto, G., Persico, V., & Pescapé, A. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2018.02.008 · Cited by 504
Intelligence in the Internet of Medical Things era: A systematic review of current and future trends
Al-Turjman, F., Nawaz, M. H., & Ulusar, U. D. (2020) · Computer Communications · doi:10.1016/j.comcom.2019.12.030 · Cited by 339
A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining
Islam, M. S., Hasan, M. M., Wang, X., Germack, H. D., & Noor-E-Alam, M. (2018) · Healthcare · doi:10.3390/healthcare6020054 · Cited by 267
Computational Health Informatics in the Big Data Age: A Survey
Fang, R., Pouyanfar, S., Yang, Y., Chen, S.-C., & Iyengar, S. S. (2017) · ACM Computing Surveys · doi:10.1145/2932707 · Cited by 210
Gossec, L., Guyard, F., Leroy, D., Lafargue, T., Seiler, M., Jacquemin, C., Molto, A., Sellam, J., Foltz, V., Gandjbakhch, F., et al. (2019) · Arthritis Care & Research · doi:10.1002/acr.23768 · Cited by 143
Metadata
- Authors
- Tomar, D., & Agarwal, S.
- Venue
- International Journal of Bio-Science and Bio-Technology
- Publisher
- Global Vision Press
- Published
- 2013-10-31
- Volume
- 5
- Issue
- 5
- Pages
- 241-266
- ISSN
- 2208-9810 (ISSN-L)
- 2233-7849
- OpenAlex
- W2097686533
- Cited by
- 483
- References
- 5
- FWCI
- 46.75
- Type
- journal article
- Language
- en
- Open access
- hybrid
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