A framework for childhood obesity classifications and predictions using NBtree

Obesity is a common issue nowadays. The numbers of obese people are increasing every year. There are evidences that childhood obesity persists into adulthood. Predicting obesity at an early age is both useful and important because preventive measures and proper interventions can be applied if the children indicated a high risk of obesity. However, the prediction of childhood obesity is a difficult task. Many ways and techniques such as assessment of body composition, data mining techniques, and logistic regression have been applied to predict childhood obesity, but only a few managed to produce accurate results. The numbers of efforts on childhood obesity prediction need to be increased and the techniques used should be improvised. The initial stage of this study involves collecting data from primary sources: parents, children and caretaker. Then, we identify risk factors such as parental obesity and education, children lifestyle and habits, and environment influences, and proposes a framework of childhood obesity prediction using NBtree.