Ensemble Learning Models for Predicting the Gaming Addiction Behaviours of Adolescents

This paper proposes: 1) to create a prediction model for the game addiction of adolescents using six data mining algorithms; 2) to optimise the models by adjusting the parameters; 3) to create an ensemble model. Bagging and boosting algorithms were investigated for improving the models. Data were collected from eight Northern Rajabhat Universities in Thailand. The results found that bagging with neural network had shown the highest performance with an accuracy of 99.35%, followed by the boosting with neural network (99.02%), the model with the best-optimised parameters of the neural network algorithm achieved by adjusting the learning rate. The best model was used to develop a web application for predicting the gaming addiction behaviours of adolescents, which would contribute to solve the problem.