Title: Ensemble learning models for predicting the gaming addiction behaviours of adolescents

Authors: Nongyao Nai-arun; Warachanan Choothong

Addresses: Faculty of Science and Technology, Phranakhon Si Ayutthaya Rajabhat University, Phranakhon Si Ayutthaya, Thailand ' Faculty of Science and Technology, Nakhon Sawan Rajabhat University, Nakhon Sawan, Thailand

Abstract: 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.

Keywords: classification; ensemble learning; bagging; boosting; neural network; random forest; optimisation; gaming addiction behaviours.

DOI: 10.1504/IJDMMM.2025.144623

International Journal of Data Mining, Modelling and Management, 2025 Vol.17 No.1, pp.103 - 125

Received: 14 Dec 2023
Accepted: 17 May 2024
Published online: 25 Feb 2025 *

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