PREDICTION OF COMPUTER GAME ADDICTION IN CHILDREN USING DEVELOPED ARTIFICIAL NEURAL NETWORKS (ANN) AND MULTIPLE LINEAR REGRESSION (MLR) MODELS

Estimation of game addiction in children plays a major role the mental and physical development child. Therefore, Various scales are used to examine various input parameters (Age, Gender, Daily play time, etc.) employed scales. The purpose this study is project system that estimates whether child addicted when looking at parameters. Artificial Neural Networks (ANN) Multiple Linear Regression (MLR) techniques were design system. In order measure predictive performance developed models, Root Mean Squared Error (RMSE), Correlation Coefficient (R) criteria examined respectively it was observed model by ANN predicted CGA with high accuracy.