Ensemble vote approach for predicting primary tumors using data mining

Primary tumor is a neoplasm which in clinical parlance is regarded as malignant, arising in one site and capable of giving rise to metastatic tumors. Primary tumor disease is a major health problem in today´s time. This paper aims at analyzing various data mining techniques for primary tumor prediction. The observations reveal that the hybrid approach of any three classifiers using Vote ensemble technique on resampled dataset has outperformed over all other single data mining classifiers.The study considers total 19 attributes by adding `small-intestine´ an attribute in the original primary tumor dataset. By addition of `small-intestine´ attribute, ensemble Vote classifier achieves high accuracy of 94.01% even when the data set contains missing values. Evaluations and results are carried out with 10-fold cross validation using Weka 3-6-10.