  Complex survey designs are commonly employed in many medical cohorts. In such
scenarios, developing case-specific predictive risk score models that reflect
the unique characteristics of the study design is essential. This approach is
key to minimizing potential selective biases in results. The objectives of this
paper are: (i) To propose a general predictive framework for regression and
classification using neural network (NN) modeling, which incorporates survey
weights into the estimation process; (ii) To introduce an uncertainty
quantification algorithm for model prediction, tailored for data from complex
survey designs; (iii) To apply this method in developing robust risk score
models to assess the risk of Diabetes Mellitus in the US population, utilizing
data from the NHANES 2011-2014 cohort. The theoretical properties of our
estimators are designed to ensure minimal bias and the statistical consistency,
thereby ensuring that our models yield reliable predictions and contribute
novel scientific insights in diabetes research. While focused on diabetes, this
NN predictive framework is adaptable to create clinical models for a diverse
range of diseases and medical cohorts. The software and the data used in this
paper is publicly available on GitHub.
