Study of Ensemble Classifier for Prediction in Health Care DataAuthor : S. Sathurthi, R. Kamalakannan and T. Rameshkumar
Volume 8 No.1 Special Issue:February 2019 pp 36-37
Electronic health record systems are adapted in a good deal of health care facility to improve the quality of patient care which is maintained electronically. Developing a disease prediction model for health care system can help us to overcome the problem of medical distress. In this study, we suggest ensemble technology and statistical methods to search through massive amounts of information, analyzing it to predict outcomes for individual patients. Using Weka tool, breast-cancer and diabetes medical datasets have experimented with ensemble classifier.
Ensemble, Random Forest, Bagging and Boosting
 T. G. Dietterich, “Ensemble methods in machine learning”, In: Proceedings of Multiple Classifier System, Springer, Vol. 18, pp. 1–15, 2000
 L. Breiman: “Bagging predictors”, Mach. Learn., Vol. 24, No. 2, pp.123–140, 1996.
 Thomas G. Dietterich, “An Experimental Comparison of Three Methods for Constructing of Decision Trees: Bagging, Boosting, and Randomization”, Machine Learning, Vol.40, pp. 139–157, 2000.
 Y. Freund, and R.E. Schapire, “Decision-theoretic generalization of on-line learning and an application to boosting”, J. Computer and System Sciences Vol. 55, No. 1, pp.119–139, 1997.
 LiorRokach, “Ensemble Methods for Classifiers”, Data Mining and Knowledge Discovery Handbook, pp. 957-980.
 L. Breiman. “Random forests”, Machine Learning, Vol. 45, No. 1, pp.5–32, 2001.
 D. Wolpert, “Stacked generalization”, Neural Networks, Vol. 5, No. 2, pp. 241–260, 1992.