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
Giabbanelli, Philippe J
Public Health Nutrition
Cambridge University Press
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Giabbanelli, P. J., & Adams, J. (2016). 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. Public Health Nutrition, 19 1543-1551. https://doi.org/10.1017/S1368980016000185
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.
Data mining, diet, dietary assessment, dietary pattern analysis, nutrition
The work was undertaken by the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence. Both authors gratefully acknowledge funding from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council (MRC), the National Institute for Health Research (NIHR), and the Wellcome Trust, under the auspices of the UK Clinical Research Collaboration (reference MR/K023187/1).
Wellcome Trust (087636/Z/08/Z)
External DOI: https://doi.org/10.1017/S1368980016000185
This record's URL: https://www.repository.cam.ac.uk/handle/1810/253358
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