JMIR Publications

ABSTRACT

Background:

: Depression and anxiety symptoms in early childhood have a major effect on children's mental health growth and cognitive development. Studying the effect of mental health problems on cognitive development has gained researchers' attention for the last two decades

Objective:

In this paper, we seek to use machine learning techniques to predict the risk factors associated with school children's depression and anxiety

Methods:

The study data consisted of 5685 students in grades 5-9, aged 10-17 years, studying at public and refugee schools in the West Bank. The data were collected using the health behaviors school children questionnaire in the 2012-2013 academic year and analyzed using machine learning to predict the risk factors associated with student mental health symptoms. Five machine learning techniques (Random Forest, Neural Network, Decision Tree, Support Vector Machine, and Naïve Bayes) were used for the prediction.

Results:

The results indicated that the Random Forest model had the highest accuracy levels (72.6%, 68.5%) for depression and anxiety respectively. Thus, the Random Forest had the best performance in classifying and predicting the student's depression and anxiety. The results showed that school violence and bullying, home violence, academic performance, and family income were the most important factors affecting depression and anxiety scales

Conclusions:

Overall, machine learning proved to be an efficient tool for identifying and predicting the associated factors that influence student depression and anxiety. The deployment of machine learning within the school information systems might facilitate the development of health prevention and intervention programs that will enhance students’ mental health and cognitive development.