Enhancing the Performance of Machine Learning Classifiers by Hyperparameter Optimization in Detecting Anxiety Levels of Online Gamers

Abstract

Mental health is an essential component of human life and maintaining a healthy state is often overlooked in today’s world. While playing games online is a fantastic method to reduce stress, it imposes a negative impact on people’s mental health. For instance, anxiety disorders are a group of mental illnesses marked by intense emotions of fear and anxiety which are witnessed in online gamers to a greater extent. To aid the identification process of anxiety levels, machine learning algorithms have emerged as a handy tool. In this paper, the anxiety levels of online gamers are detected by utilizing a dataset from Kaggle by nine machine learning algorithms. The performances of the ML models have been observed through Python simulation, and comprehensive comparative analysis has been shown for both hyperparameter tuning and without hyperparameter tuning. Random search cross-validation has been brought into action for tuning the hyper parameters. In terms of several performance measures such as accuracy, precision, recall, F1-Score, and ROC-AUC, satisfactory results have been observed. It is observed that Multilayer perceptron (MLP) outperformed the other classifiers with an accuracy of 99.96%. However, Support Vector Machine (SVM) depicted promising accuracy of 99.43% whereas Gradient Boosting (GB) and XGBoost (XGB) depicted 98.54% and 98.04% accuracy respectively. Therefore, it can be concluded that with proper implementation of the ML-based diagnosis system, it is possible to detect the anxiety level of online gamers which can assist in having a deeper understanding of behaviors and impact of online gaming in daily life.