A Machine Learning Based Approach to Predict Online Gaming Addiction in the Context of Bangladesh

Abstract

Online games are hazardous to one's health and well-being. Addiction to video games has now become a concern to Bangladeshi youth. Our lives are negatively impacted by online gaming. We must keep an eye on our country's youth to ensure that they do not become addicted to any kind of internet gaming. We must refrain from playing games in order to avoid being hooked on them. Machine learning will be used to forecast the likelihood of becoming addicted to video games. We begin by reviewing relevant studies, journals, and internet publications, and then we speak with online gaming addictions. We discovered certain common elements that are linked to gaming addiction and Lemmen’s gaming addiction scale to measure the whether a person is addicted or not. Then, based on those criteria, such as age, gender, mental pressure, and so on, we collect data. We gather information from both addicts and nonaddicts. There are two possible outcomes: We processed all of the data once it was collected and developed a processed dataset. On our processed dataset, we used machine learning techniques. In different predictions and detection systems, machine learning, artificial intelligence and deep learning are applied. KNN (K-Nearest Neighbor), LR (Logistic Regression), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Random Forest (RF), Adaptive Boosting (ADA Boosting), Decision Tree (DT), XGBOOST and Gradient Boosting (GB) classifier are some of the methods we utilize. Adaptive Boosting outperformed the other nine methods in our study. All the nine classifiers predict with high accuracy and among them, Adaptive Boosting (ADA Boosting) gives the maximum accuracy which is 93.00%.