The Comparison of Generalized Additive Model with Artificial Hierarchical Neural Network in the Analysis of Pharmaceutical Data

Abstract

Although the Generalized Additive Model (GAM) is known as a superior nonparametric regression method, there have been only a few applications, especially in the fields of chemistry and pharmaceutical sciences. GAM can be applied to nonlinear problems that can also be solved using the hierarchical Artificial Neural Network (ANN) method. In this study, GAM was compared with ANN in regression, classification, and prediction power using artificial and actual pharmaceutical data sets. The results show that GAM and ANN have similar regression/classification/prediction powers. Considering the fact that additive models simply visualize the relationship between a predictor variable and a response variable, GAM can be applied to data sets in the pharmaceutical sciences.