# Screening for Diabetes Mellitus in the U.S. Population Using Neural Network Models and Complex Survey Designs

arXiv: https://arxiv.org/abs/2403.19752 · HTML: https://mlsanity.com/paper/2403.19752/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Conformal prediction beyond exchangeability](https://mlsanity.com/paper/2202.13415/) (2022)
- [Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks](https://mlsanity.com/paper/1703.02930/) (2017)
- [A Selective Overview of Deep Learning](https://mlsanity.com/paper/1904.05526/) (2019)
- [Distributional data analysis of accelerometer data from the NHANES database using nonparametric survey regression models](https://mlsanity.com/paper/2104.01165/) (2021)
- [Response-adaptive randomization in clinical trials: from myths to practical considerations](https://mlsanity.com/paper/2005.00564/) (2020)

## Built on by (most cited first, 3 of 3)

- [Uncertainty quantification in metric spaces](https://mlsanity.com/paper/2405.05110/) (2024)
- [Variable Selection Methods for Multivariate, Functional, and Complex Biomedical Data in the AI Age](https://mlsanity.com/paper/2501.06868/) (2025)
- [Distributional Random Forests for Complex Survey Designs](https://mlsanity.com/paper/2512.08179/) (2025)

## Abstract (arXiv)

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 for minimizing selective biases in the statistical results. The objectives of this paper are to: (i) propose a general predictive framework for regression and classification using neural network (NN) modeling that incorporates survey weights into the estimation process; (ii) introduce an uncertainty quantification algorithm for model prediction tailored to data from complex survey designs; and (iii) apply this method to develop robust risk score models for assessing the risk of Diabetes Mellitus in the US population, utilizing data from the NHANES 2011-2014 cohort. The results indicate that models of varying complexity, each utilizing a different set of variables, demonstrate different discriminative power for predicting diabetes (with different economic cost), yet yield generalizable results at the population level. Although the focus is on diabetes, this NN predictive framework is adaptable for developing clinical models across a diverse range of diseases and medical cohorts. The software and data used in this paper are publicly available on GitHub.