Publication: Childhood Obesity And Overweight Predictions Using Hybrid Data Mining Techniques
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Date
2013-02
Authors
Adnan, Muhamad Hariz Muhamad
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Abstract
Childhood obesity has become a worrying global epidemic. Evidences show that childhood obesity persists into adulthood. Therefore, predicting obesity at an early age is both useful and important. The objectives of this study are to identify significant risk factors and protective factors of childhood obesity in Malaysia; to select and propose parameters for prediction; to investigate suitable data mining techniques for childhood obesity and overweight predictions; and to propose hybrid data mining techniques to increase the sensitivity of predictions. This study consisted of five stages that are risk factors reviews, data collections, parameter selection, empirical study, propose and evaluation of hybrid techniques. The factors that are related to the Malaysian children are: sex, catch-up growth, premature birth, adiposity rebound, breastfeeding duration, soup, sandwich, snack or chocolate eaten outdoors, excessive television (TV) watching, eating junk food, eat junk food in front of TV, warm meals for supper, eating fried food and fruits, physical activity, duration of sleep, number of sibling, and parents BMI. Three hybrid techniques have been proposed. The first technique is a hybrid of Naïve Bayes (NB) and Classification and Regression Tree (CART) in a NB-Tree framework; the second technique is using the CART for variable selection and NB for prediction (CART Selection-NB Classification); and the third technique integrates mean value calculation and Euclidean Distances classification into the second technique (Mean-Euclidean
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Childhood Obesity And Overweight Predictions Using Hybrid Data Mining Techniques