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 …

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

Background: Childhood obesity has emerged as a significant public health challenge, with
long-term implications that often extend into adulthood, increasing the susceptibility to …

Background Digital health interventions based on tools for Computerized Decision Support
(CDS) and Machine Learning (ML), which take advantage of new information, sensing and …

Child obesity is a serious problem in our modern world and shows an increase of 60% since
1990. Due to time and cost intensity of traditional therapy programs, scientists started to …

Obesity is a major public health concern. Multidisciplinary pediatric weight management
programs are considered standard treatment for children with obesity who are not able to be …

Childhood obesity is a public health concern in the United States. Consequences of
childhood obesity include metabolic disease and heart, lung, kidney, and other health …

Background Because of the strong link between childhood obesity and adulthood obesity
comorbidities, and the difficulty in decreasing body mass index (BMI) later in life, effective …

Childhood obesity is a significant health problem in current societies that is increasing at an
alarming way among population of all ages. To date, studies on the effectiveness of …