A Novel Application for Combining CASs and Datasets to Produce Increased Accuracy in Modeling and Predicting Cancer Recurrence

“Ensemble processing” combines the results (outputs) of several different models, each “looking at” a disease from a different perspective. A number of different methods are available to support ensemble processing: (1) averaging, (2) weighted-averaging,(3) Adaboost, and (4) other processing methods that use gate variables in forming a “tree structure”. Gate variables are used here as an integral part of the Expectation operation in a maximum likelihood estimator. This paper presents the application of a “Generalized Regression Neural Network Predictive Model,” called the “GRNN oracle,” that takes advantage(s) of correlation(s) (synergies) that exist between intelligent predictive input model outputs by combining them (at the variance level) for generating both clinical and microarray lung cancer data to improve cancer recurrence modeling and predictive performance, when compared to any one output taken alone. The hypothesis is: Given a validation data set that contains a sufficient sample size, then the GRNN oracle will provide a synergistic combination of output data which is superior in predictive performance accuracy (as measured by an ROC analysis) when compared to all input intelligent models, taken individually. This paper will discuss the results of our work in evaluating the validity of this hypothesis.