Challenges for Predictive Modeling with Neural Network Techniques using
  Error-Prone Dietary Intake Data
Dylan Spicker Amir Nazemi Joy Hutchinson Paul Fieguth Sharon I. Kirkpatrick Michael Wallace Kevin W. Dodd
Dietary intake data are routinely drawn upon to explore diet-health
relationships. However, these data are often subject to measurement error,
distorting the true 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 machine learning
techniques, and in particular, neural networks. Neural networks are
computational models that are able to capture highly complex, nonlinear
relationships so long as sufficient data are available. While these models have
been applied in many domains, the impacts of measurement error on the
performance of predictive modeling has not been systematically investigated.
However, dietary intake data are typically collected using self-report methods
and are prone to large amounts of measurement error. In this work, we
demonstrate the ways in which measurement error erodes the performance of
neural networks, and illustrate the care that is required for leveraging these
models in the presence of error. We demonstrate the role that sample size and
replicate measurements play on model performance, indicate a motivation for the
investigation of transformations to additivity, and illustrate the caution
required to prevent model overfitting. While the past performance of neural
networks across various domains make them an attractive candidate for examining
diet-health relationships, our work demonstrates that substantial care and
further methodological development are both required to observe increased
predictive performance when applying these techniques, compared to more
traditional statistical procedures.