Dietary patterns analysis using data mining method. An application to data from the CYKIDS study

Authors:
Chrystalleni Lazarou;Minas Karaolis;Antonia-Leda Matalas;Demosthenes B. Panagiotakos
Affiliations:
Harokopio University, Department of Nutrition and Dietetics, 70 Eleftheriou Venizelou Str., 17671 Athens, Greece;University of Cyprus, Department of Computer Science, Kallipoleos Avenue, Nicosia, Cyprus;Harokopio University, Department of Nutrition and Dietetics, 70 Eleftheriou Venizelou Str., 17671 Athens, Greece;Harokopio University, Department of Nutrition and Dietetics, 70 Eleftheriou Venizelou Str., 17671 Athens, Greece
Venue:
Computer Methods and Programs in Biomedicine
Year:
2012

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Abstract

Data mining is a computational method that permits the extraction of patterns from large databases. We applied the data mining approach in data from 1140 children (9-13years), in order to derive dietary habits related to children's obesity status. Rules emerged via data mining approach revealed the detrimental influence of the increased consumption of soft dinks, delicatessen meat, sweets, fried and junk food. For example, frequent (3-5times/week) consumption of all these foods increases the risk for being obese by 75%, whereas in children who have a similar dietary pattern, but eat 2times/week fish and seafood the risk for obesity is reduced by 33%. In conclusion patterns revealed from data mining technique refer to specific groups of children and demonstrate the effect on the risk associated with obesity status when a single dietary habit might be modified. Thus, a more individualized approach when translating public health messages could be achieved.