  High accuracy in cancer prediction is important to improve the quality of the
treatment and to improve the rate of survivability of patients. As the data
volume is increasing rapidly in the healthcare research, the analytical
challenge exists in double. The use of effective sampling technique in
classification algorithms always yields good prediction accuracy. The SEER
public use cancer database provides various prominent class labels for
prognosis prediction. The main objective of this paper is to find the effect of
sampling techniques in classifying the prognosis variable and propose an ideal
sampling method based on the outcome of the experimentation. In the first phase
of this work the traditional random sampling and stratified sampling techniques
have been used. At the next level the balanced stratified sampling with
variations as per the choice of the prognosis class labels have been tested.
Much of the initial time has been focused on performing the pre_processing of
the SEER data set. The classification model for experimentation has been built
using the breast cancer, respiratory cancer and mixed cancer data sets with
three traditional classifiers namely Decision Tree, Naive Bayes and K-Nearest
Neighbor. The three prognosis factors survival, stage and metastasis have been
used as class labels for experimental comparisons. The results shows a steady
increase in the prediction accuracy of balanced stratified model as the sample
size increases, but the traditional approach fluctuates before the optimum
results.
