A Significance of Data Mining in the field of Healthcare Sector

Authors

  • Swati C. Tawalare  Research Scholar, Department of Computer Science & Engineering, Babasaheb Naik College of Engineering, Pusad, India
  • Dr. S. Y. Amdani  Associate Professors, Department of Computer Science & Engineering, Babasaheb Naik College of Engineering, Pusad, India

Keywords:

Data Mining, Healthcare Sector, Quality Service, MRI, ECG

Abstract

Currently, there's huge quantum of data being collected and stored in databases far and wide across the globe running into terabytes of data and the tendency is to keep adding time after time. Moment, the healthcare assiduity which is one of the largest diligences throughout the world includes medical diligence having the large quantities of health- related and medical-affiliated data. It also includes thousands of hospitals, conventions and other types of installations that give primary, secondary and tertiary situations of care. The delivery of healthcare service is the most visible part of any healthcare system, both to druggies and the general public. Accurate, early and error-free opinion and treatment given to cases has been a major issue stressed in medical service currently. Quality service in health care field implies diagnosing cases rightly and administering treatments that are effective. To achieve a correct and cost-effective treatment, a system can be developed to fulfill the task.

References

  1. Kamma, S., Parul, B., Sandeep, D. and Sudluv (2016) Analysis of Application of Data Mining Techniques in Healthcare. International Journal of Computer Applications, 148, 16-21. https://doi.org/10.5120/ijca2016911011
  2. Koh, C.H. and Tan, G. (2011) Data Mining Applications in Healthcare. Journal of Healthcare Information Management, 19, 64-72.
  3. Kaur, H. and Wasan, S.K. (2006) Empirical Study on Applications of Data Mining
  4. Techniques in Healthcare. Journal of Computer Science, 2, 194-200. https://doi.org/10.3844/jcssp.2006.194.200
  5. Obenshain, M.K. (2004) Application of Data Mining Techniques to Healthcare Data. Infection Control & Hospital Epidemiology, 25, 690-695. https://doi.org/10.1086/502460
  6. Liao, S.H., Chu, P.-H. and Hsiao, P.-Y. (2012) Data Mining Techniques and Applications—A Decade Review from 2000 to 2011. Expert Systems with Applications, 39, 11303-11311. https://doi.org/10.1016/j.eswa.2012.02.063
  7. Ubochi, C.I. (2017) Data Mining Technique for Detecting Cardiovascular Diseases. An Unpublished Msc Thesis.
  8. Savage, N. (2011) Mining Data for Better Medicine. MIT Technology Review, 38, 235-237.
  9. Joshi, S., Deepashenoy, P., Venugopal, K.R. and Patnaik, L.M. (2010) Data Analysis and Classification of Various Stages of Dementia Adopting Rough Sets Theory. International Journal on Information Processing, 4, 86-89.
  10. Fayyad, U.M., et al. (1996) From Data Mining to Knowledge Discovery in Databases. AAAI Press/The MIT Press, Cambridge.
  11. Rizzi, S., Bertino, E., Catania, B., Golfarelli, M., Halkidi, M., Terrovitis, M., Vassiliadis, P., Vazirgiannis, M. and Vrachnos, E. (2003) Towards a Logical Model for Patterns in ER. Springer, Chicago, Vol. 2813, 77-90. https://doi.org/10.1007/978-3-540-39648-2_9
  12. Ntoutsi, I. (2008) Similarity Issues in Data Mining—Methodologies and Techniques. University of Piraeus, Piraeus, 31-32.
  13. Li, J.-S., Yu, H.-Y. and Zhang, X.-G. (2011) Data Mining in Hospital Information System. In: Funatsu, K., Ed., New Fundamental Technologies in Data Mining, InTech, Shanghai, Vol. 1, 143-156.
  14. Balasunda, V., Devi, T. and Saravanan, N. (2012) Development of a Data Clustering Algorithm for Predicting Heart. International Journals of Computer Applications, 48, 8-13. https://doi.org/10.5120/7358-0095
  15. Han, J. and Kamber, M. (2006) Data Mining Concepts and Techniques. 2nd
  16. Edition, Morgan Kaufmann Publishers Inc., Burlington, 55-86.
  17. Chauraisa, V. and Pal, S. (2013) Data Mining Approach to Detect Heart Diseases. International Journal of Advanced Computer Science and Information Technology, 56-66.
  18. Aflori, C. and Craus, M. (2007) Grid Implementation of the Apriori Algorithm.
  19. Advances in Engineering Software, 38, 295-300. https://doi.org/10.1016/j.advengsoft.2006.08.011
  20. Deulkar, D.S. and Deshmukh, R.R. (2016) Data Mining Classification. Imperial Journal of Interdisciplinary Research, 2.
  21. Palaniappan, S. and Awang, R. (2008) Intelligent Heart Disease Prediction System Using Data Mining Techniques. IEEE Conference on Computer Systems and Applications, Doha, 31 March 2008-4 April 2008, 108-115. https://doi.org/10.1109/AICCSA.2008.4493524
  22. Srinivas, K.B., Rani, K. and Govrdhan, A. (2010) Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks. International Journal on
  23. Computer Science and Engineering, 2, 250-255.
  24. Ahmed, P., Qamar, S. and Rizvi, S.Q.A. (2015) Techniques of Data Mining in
  25. Healthcare: A Review. International Journal of Computer Applications, 120, 38-50. https://doi.org/10.5120/21307-4126
  26. Durairaj, M. and Ranjani, V. (2013) Data Mining Applications in Healthcare Sector: A Study. International Journal of Scientific and Technology Research, 2, 29-35.
  27. Vapnik, V. (1998) The Support Vector Method of Function Estimation. In: Suykens, J.A.K. and Vandewalle, J., Eds., Nonlinear Modeling, Springer, Berlin, 55-85. https://doi.org/10.1007/978-1-4615-5703-6_3
  28. Burges, C.J.C. (1998) A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2, 121-167. https://doi.org/10.1023/A:1009715923555
  29. Robertson, J. (2012) Data Mining in Doctor’s Office Helps Solve Medical Mysteries. Vol. 1, Wal-Mart or Western Union United Healthcare Corp., New York.
  30. Ionita, I. and Ionita, L. (2016) Applying Data Mining Techniques in Healthcare. Studies in Informatics and Control, 25, 385-394. https://doi.org/10.24846/v25i3y201612
  31. Canlas Jr., R.D. (2015) Data Mining in Healthcare: Current Applications and Issues.
  32. Ranjan, J. (2007) Application of Data Mining Techniques in Pharmaceutical Industry. Journal of Theoretical and Applied Information Technology, 3, 61-67.
  33. Diwani, S., Mishol, S., Kayange, D.S., Machuve, D. and Sam, A. (2013) Overview Applications of Data Mining in Health Care: The Case Study of Arusha Region.
  34. International Journal of Computational Engineering Research, 3, 73-77.
  35. Desikan, P., Hsu, K.W. and Srivastava, J. (2011) Data Mining for Healthcare Management. SIAM International Conference on Data Mining, Arizona.
  36. Page, D. and Craven, M. (2016) Biological Applications of Multi-Relational Data Mining. http://www.kdd.org/exploration_files/Page.pdf
  37. Brossette, S.E., Sprague, A.P., Hardin, M.K., Waites, B., Jones, W.T. and Moser, S.A. (1998) Association Rules and Data Mining in Hospital Infection Control and Public Health Surveillance. Journal of the American Medical Informatics Association, 5, 373-381. https://doi.org/10.1136/jamia.1998.0050373
  38. Ridinger, M. (2002) American Healthways Uses SAS to Improve Patient Care. DM Review, 12, Article No. 139.
  39. Gharehchopogh, F.S., Molany, M. and Mokri, F.D. (2013) Using Artificial Neural Network in Diagnosis of Thyroid Disease: A Case Study. International Journal on Computational Sciences & Applications, 3, 49-61.
  40. Shukla, A. and Kaur, P. (2009) Diagnosis of Thyroid Disorders Using Artificial Neural Networks. IEEE International Advance Computing Conference, Patiala, 6-7 March 2009, 1016-1020. https://doi.org/10.1109/IADCC.2009.4809154
  41. Prerana, E., Sehgal, P. and Taneja, K. (2015) Predictive Data Mining for Diagnosis of Thyroid Disease Using Neural Network. International Journal of Research in Management, Science & Technology, 3, 75-80.
  42. Chang, C.Y., Tsai, M.F. and Chen, S.J. (2008) Classification of the Thyroid Nodules Using Support Vector Machines. International Joint Conference on Neural Networks, Hong Kong, 1-8 June 2008, 3093-3098. https://doi.org/10.1109/IJCNN.2008.4634235
  43. Upadhayay, A., Shukla, S. and Kumar, S. (2013) Empirical Comparison by Data Mining Classification Algorithms (C 4.5 & C 5.0) for Thyroid Cancer Data Set. International Journal of Computer Science & Communication Networks, 3, 64-68.
  44. Keleş, A. and Keleş, A. (2008) ESTDD: Expert System for Thyroid Diseases Diagnosis. Expert Systems with Applications, 34, 242-246. https://doi.org/10.1016/j.eswa.2006.09.028
  45. Chen, H.L., Yang, B., Wang, G., Liu, J., Chen, Y.D. and Liu, D.Y. (2012) A Three Stage Expert System Based on Support Vector Machines for Thyroid Disease Diagnosis. Journal of Medical Systems, 36, 1953-1963. https://doi.org/10.1007/s10916-011-9655-8
  46. UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/machinelearning-databases/thyroid-disease

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Published

2024-02-20

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Section

Research Articles

How to Cite

[1]
Swati C. Tawalare, Dr. S. Y. Amdani, " A Significance of Data Mining in the field of Healthcare Sector" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 10, Issue 1, pp.217-230, January-February-2024.