  This paper introduces a novel paradigm to impute missing data that combines a
decision tree with an auto-associative neural network (AANN) based model and a
principal component analysis-neural network (PCA-NN) based model. For each
model, the decision tree is used to predict search bounds for a genetic
algorithm that minimize an error function derived from the respective model.
The models' ability to impute missing data is tested and compared using HIV
sero-prevalance data. Results indicate an average increase in accuracy of 13%
with the AANN based model's average accuracy increasing from 75.8% to 86.3%
while that of the PCA-NN based model increasing from 66.1% to 81.6%.
