Cancer Prognosis Prediction Using Balanced Stratified Sampling
Cancer Prognosis Prediction Using Balanced Stratified Sampling J.S.Saleema1, N.Bhagawathi2, S.Monica2, P.Deepa Shenoy2, K.R.Venugopal2 and L.M.Patnaik3 1Christ University, India 2University Visvesvaraya College of Engineering, India 3Indian Institute of Science, India ABSTRACT 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 traditio...