Missing data approaches for rational decision making: Application to antenatal data

Abstract

This chapter introduces missing data estimation for rational decision making. In this chapter it is assumed that there is a fixed topological characteristic between the variables required to make a rational decision and the actual rational decision. This, therefore, implies that rational decision making can be viewed as a missing data in a topology that includes both the action variables and the decision. This technique is applied using an autoassociative multi-layer perceptron network trained using scaled conjugate method and the missing data is estimated using genetic algorithm. This technique is used to predict HIV status of a subject given the demographic characteristics.