Frequent Patterns of Childhood Overweight from Longitudinal Data on Parental and Early-Life of Infants Health

Childhood obesity is considered one of the main public health concerns. Research in the field of obesity detection and prevention is moving towards promising solutions thanks to the use of Artificial Intelligence applied to data from cohorts of children. Previous studies have analyzed the data without taking into account the relationship of data regarding when they are collected. In this work, frequent pattern mining is used to find the risk factors of childhood obesity, taking into account the relationship among the data gathered in different visits. The experiments carried out on the data collected from 386 children from Girona and Figueres (Spain) demonstrate the relevance of discriminant frequent patterns for childhood overweight prediction.