Backward model building for nonparametric discrimination and classification of fatty liver cases

Abstract

A backward-model-building algorithm for discrimination of two populations in a nonparametric setting has been recently proposed by Padmanaban and William (2016a). This approach is applicable without assumptions unlike the traditional approaches of constructing discriminant models. As an application of this approach, we consider the discrimination and classification of fatty-liver cases from non-fatty-liver cases through some observable variables that are generally thought of as factors associated with the health of the liver. The discriminant model is developed with a sample of 160 cases drawn from a case-control study. The resulting discriminant model is compared to binary logistic regression modelvis-a-vis discriminatory capacity as measured by Kolmogorov-Smirnov Statistic.