Abstract:
Using mobile game addiction as a research focus, this case study examines how this digital
disorder affects the academic performance and social life of university students. The study
objectives include applying machine learning analysis for predicting the degree of mobile
game addiction and creating a prediction model that will facilitate early intervention. One
method used in this study was a cross-sectional survey of 600 university students on 17
variables: gaming tendency and several mood and social consequences. The obtained
dataset underwent some preprocessing and encoding in order to be tested for different ml
algorithms. Out of all the models, Random Forest Classifier achieved the highest accuracy
of 96.45 % and Gradient Boosting Classifier Test set accuracy was 96.04% and for
Decision Tree Classifier was 95.04%. Logistic Regression and GaussianNB had the lowest
ranking, scoring 79.58% and 74.37%, respectively. Originally, the study result showed that
feature selection and data preprocessing had a dramatic impact on model performance.
Dependents is well classified with the Random Forest model and the wrongly classified
addiction levels are demographically misclassified. Based on the study, it is critical to note
that the various machine learning techniques can assist in identifying learners who require
assistance concerning possible detrimental effects on their mental wellbeing. The
subsequent research should recruit more participants and from a diverse population, while
developing more intricate models for the improved prediction of depression risks.
