The epidemiological question of concern here is “can young children at risk of obesity be
identified from their early growth records?” Pilot work using logistic regression to predict …

Objectives: This paper aims to predict childhood obesity after age two, using only data
collected prior to the second birthday by a clinical decision support system called CHICA …

In this paper we present data mining and its utilization for childhood obesity prediction. Data
mining was widely used in many childhood obesity prediction systems. Predicting obesity at …

The prevalence of childhood and adolescence overweight an obesity is raising at an
alarming rate in many countries. This poses a serious threat to the current and near-future …

Individuals developing signs of weight gain or obesity are at a risk of developing serious
illnesses such as type 2 diabetes, respiratory problems, coronary heart disease and stroke …

Obesity is a major global concern with more than 2.1 billion people overweight or obese
worldwide which amounts to almost 30% of the global population. If the current trend …

Previous studies demonstrate the feasibility of predicting obesity using various machine
learning techniques; however, these studies do not address the limitations of these methods …

Obesity has become a global epidemic that has doubled since 1980, with serious
consequences for health in children, teenagers and adults. Obesity is a problem has been …

Objective This study compares seven machine learning models developed to predict
childhood obesity from age> 2 to≤ 7 years using Electronic Healthcare Record (EHR) data …

Background Advancements in knowledge of obesity aetiology and mobile phone technology
have created the opportunity to develop an electronic tool to predict an infant's risk of …