Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review

Abstract

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 health systems, given the association of these conditions with different comorbidities (cardiovascular diseases, type II diabetes, and metabolic syndrome) and even death. In order to design appropriate strategies for its prevention, as well as understand its origins, the development of predictive models for childhood/adolescent overweight/obesity and related outcomes is of extreme value. Obesity has a complex etiology, and in the case of childhood and adolescence obesity, this etiology includes also specific factors like (pre)-gestational ones; weaning; and the huge anthropometric, metabolic, and hormonal changes that during this period the body suffers. In this way, Machine Learning models are becoming extremely useful tools in this area, given their excellent predictive power; ability to model complex, nonlinear relationships between variables; and capacity to deal with high-dimensional data typical in this area. This is especially important given the recent appearance of large repositories of Electronic Health Records (EHR) that allow the development of models using datasets with many instances and predictor variables, from which Deep Learning variants can generate extremely accurate predictions. In the current work, the area of Machine Learning models to predict childhood and adolescent obesity and related outcomes is comprehensively and critically reviewed, including the latest ones using Deep Learning with EHR. These models are compared with the traditional statistical ones that used mainly logistic regression. The main features and applications appearing from these models are described, and the future opportunities are discussed.

Keywords: BMI; childhood obesity; data science; deep learning; machine learning; obesity; overweight; statistical models.

Figure 1

Measures of the performance of a binary classifier. Class labels are “+” and “−“. Predicted category by the model is represented vs the real category, for all the possible situations.

Figure 2

ROC curve of a binary classifier.

Figure 3

Depiction of Decision Tree for two variables, X₁ and X_2. R1_, R2, R3, and R4 are partitions generated by the splits s₁, s₂, and s₃. The labels for the partitions would be a function of the labels of the instances in each partition in the training set.

Figure 4

Maximum margin hyperplane for a predictor space of two variables. Two categories are perfectly classified by this hyperplane. The hashed lines indicate the maximum margin to the training set, obtained with this particular hyperplane. Training instances are presented as points in the plane, blue points corresponding to class “+” and red points to “−”. The points located at a maximum margin to the hyperplane are the support vectors, since the plane only depends on these points of the training set.

Figure 5

Typical structure of an artificial neuron and a fully connected feedforward neural network. The x_i are the predictor variables, the w_i are the weights and b is the biass.