Data Mining is getting popular in health care related research. It plays an important role for hidden information and disease patterns. The outcome helps for all those who have associated with heath care system. In this paper, we examine the application of classification based data mining techniques in healthcare data. Using medical profiles such as age, sex, pin code and disease type, it is predicted the likelihood of patients who get a large count of disease. The result of this quantitative minor research project helps to discover hidden patterns, associations between age and disease, and blood group and disease which often go unexploited.
1. E.W.T. Ngai, Yong Hu, Y.H. Wong, Yijun Chen and Xin Sun “The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature”, Decision Support Systems, Vol. 50, pp. 559–569, 2011.
2. Jinn-Yi Yeh, Tai-Hsi Wu and Chuan-Wei Tsao “Using data mining techniques to predict hospitalization of hemodialysis patients”, Decision Support Systems, Vol. 50, pp. 439–448, 2011.
3. H. C. Koh and G. Tan, “Data Mining Application in Healthcare”, Journal of Healthcare Information Management, vol. 19, no. 2, (2005).
4. Hastie, T., et al., 2009. The Elements of Statistical Learning, Data Mining, Inference and Prediction, 2nd edition. Springer, New York, USA.
5. Hosseinkhah, Fatemeh, et al. “Challenges in Data Mining on Medical Databases.” (2009): 1393-1404.
6. Baylis, Philip. “Better health care with data mining.” SPSS White Paper, UK (1999).
7. J. Nahar, T. Imam, K. S. Tickle and Y. P. Chen, “Association rule mining to detect factors which contribute to heart disease in males and females”, Expert Systems with Applications, vol. 40, pp. 1086-1093, (2013).
8. M. Kantardzic, Data mining: concepts, models, methods, and algorithms: Wiley-IEEE Press, 2003.
9. Bellazzi, R., & Zupan, B. (2008). Predictive data mining in clinical medicine: Current issues and guidelines.international journal of medical informatics 77, 81–97.