Challenges for Predictive Modeling With Neural Network Techniques Using Error-Prone Dietary Intake Data.

ABSTRACT: Dietary intake data are routinely drawn upon to explore diet-health relationships, and inform clinical practice and public health. However, these data are almost always subject to measurement error, distorting true diet-health relationships. Beyond measurement error, there are likely complex synergistic and sometimes antagonistic interactions between different dietary components, complicating the relationships between diet and health outcomes. Flexible models are required to capture the nuance that these complex interactions introduce. This complexity makes research on diet-health relationships an appealing candidate for the application of modern machine learning techniques, and in particular, neural networks. Neural networks are computational models that can capture highly complex, nonlin

SUBMITTER: Spicker D 

PROVIDER: S-EPMC11806516 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature