Predicting the Risk of Loneliness in Children and Adolescents: A Machine Learning Study.

ABSTRACT:

Background

Loneliness is increasingly emerging as a significant public health problem in children and adolescents. Predicting loneliness and finding its risk factors in children and adolescents is lacking and necessary, and would greatly help determine intervention actions.

Objective

This study aimed to find appropriate machine learning techniques to predict loneliness and its associated risk factors among schoolchildren.

Methods

The data were collected from an ongoing prospective puberty cohort that was established in Chongqing, Southwest China. This study used 822 subjects (46.84% boys, age range: 11-16) followed in 2019. Five models, (a) random forest, (b) extreme gradient boosting (XGBoost), (c) logistic regression, (d) neural network, and (e) support vector machi

SUBMITTER: Zhang J 

PROVIDER: S-EPMC11504542 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature