An Open-Source Dataset on Dietary Behaviors and DASH Eating Plan Optimization Constraints

TL;DR

This paper introduces an open-source dataset derived from NHANES and USDA data, tailored for research on dietary behaviors and optimization models, especially for DASH diet planning in hypertensive and pre-diabetic groups.

Contribution

It provides a curated, accessible dataset suitable for linear constrained optimization and inverse inference research in dietary planning and health condition-specific diets.

Findings

Dataset facilitates research on dietary optimization models.

Includes tailored data for hypertension and pre-diabetic groups.

Aims to accelerate constrained inference research in nutrition.

Abstract

Linear constrained optimization techniques have been applied to many real-world settings. In recent years, inferring the unknown parameters and functions inside an optimization model has also gained traction. This inference is often based on existing observations and/or known parameters. Consequently, such models require reliable, easily accessed, and easily interpreted examples to be evaluated. To facilitate research in such directions, we provide a modified dataset based on dietary behaviors of different groups of people, their demographics, and pre-existing conditions, among other factors. This data is gathered from the National Health and Nutrition Examination Survey (NHANES) and complemented with the nutritional data from the United States Department of Agriculture (USDA). We additionally provide tailored datasets for hypertension and pre-diabetic patients as groups of interest who may benefit from targetted diets such as the Dietary Approaches to Stop Hypertension (DASH) eating plan. The data is compiled and curated in such a way that it is suitable as input to linear optimization models. We hope that this data and its supplementary, open-accessed materials can accelerate and simplify interpretations and research on linear optimization and constrained inference models. The complete dataset can be found in the following repository: https://github.com/CSSEHealthcare/InverseLearning