Identifying small groups of foods that can predict achievement of key dietary recommendations: data mining of the UK National Diet and Nutrition Survey, 2008–12

Abstract

Objective

Many dietary assessment methods attempt to estimate total food and nutrient intake. If the intention is simply to determine whether participants achieve dietary recommendations, this leads to much redundant data. We used data mining techniques to explore the number of foods that intake information was required on to accurately predict achievement, or not, of key dietary recommendations.

Design

We built decision trees for achievement of recommendations for fruit and vegetables, sodium, fat, saturated fat and free sugars using data from a national dietary surveillance data set. Decision trees describe complex relationships between potential predictor variables (age, sex and all foods listed in the database) and outcome variables (achievement of each of the recommendations).

Setting

UK National Diet and Nutrition Survey (NDNS, 2008–12).

Subjects

The analysis included 4156 individuals.

Results

Information on consumption of 113 out of 3911 (3 %) foods, plus age and sex was required to accurately categorize individuals according to all five recommendations. The best trade-off between decision tree accuracy and number of foods included occurred at between eleven (for fruit and vegetables) and thirty-two (for fat, plus age) foods, achieving an accuracy of 72 % (for fat) to 83 % (for fruit and vegetables), with similar values for sensitivity and specificity.

Conclusions

Using information on intake of 113 foods, it is possible to predict with 72–83 % accuracy whether individuals achieve key dietary recommendations. Substantial further research is required to make use of these findings for dietary assessment.