Artificial Neural Network of the Haberman Survival Data
####################################################### ABSTRACT:
The dataset contains cases from a study that was conducted between 1958 and 1970 at the University of Chicago's Billings Hospital on the survival of patients who had undergone surgery for breast cancer.
####################################################### PROBLEM STATEMENT:
Being a binary classification problem (survived for 5 years or more following breast cancer surgery=1, or perishing within 5 years=0). Can we define, compile, fit, and evualuate our model then use it make predictions on new data?
####################################################### DATA SET INFORMATION:
The data associated with this analysis (http://archive.ics.uci.edu/ml/datasets/Haberman%27s+Survival) contains the following information:
- Age of patient at time of operation (numerical)
- Patient's year of operation (year - 1900, numerical)
- Number of positive axillary nodes detected (numerical)
- Survival status (class attribute): -- 1 = the patient survived 5 years or longer -- 2 = the patient died within 5 years (I modified the dataset, changing the 2 to a 0)
####################################################### RELEVANT PAPERS:
Haberman, S. J. (1976). Generalized Residuals for Log-Linear Models, Proceedings of the 9th International Biometrics Conference, Boston, pp. 104-122. Landwehr, J. M., Pregibon, D., and Shoemaker, A. C. (1984), Graphical Models for Assessing Logistic Regression Models (with discussion), Journal of the American Statistical Association 79: 61-83. http://rexa.info/paper/883f49956b1f22c2c7a435c7f87704e30245ea55 Lo, W.-D. (1993). Logistic Regression Trees, PhD thesis, Department of Statistics, University of Wisconsin, Madison, WI: http://rexa.info/paper/4f2ee312e02a9897433db0f1631f74b5f7bf56e6
####################################################### SOURCE: DONOR: Tjen-Sien Lim (limt '@' stat.wisc.edu)
####################################################### CITATIONS: UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. CITATIONS SOURCE: UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.