Machine Learning-Based Gaming Behavior Prediction Platform
Authors: Denis Kornev, Roozbeh Sadeghian, Stanley Nwoji, Qinghua He, Amir Gandjbbakhche, Siamak Aram
Abstract: Brain disorders caused by Gaming Addiction drastically increased due to the rise of Internet users and Internet Gaming auditory. Driven by such a tendency, in 2018, World Health Organization (WHO) and the American Medical Association (AMA) addressed this problem as a “gaming disorder” and added it to official manuals. Scientific society equipped by statistical analysis methods such as t-test, ANOVA, and neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and electroencephalography (EEG), has achieved significant success in brain mapping, examining dynamics and patterns in different conditions and stages. Nevertheless, more powerful, self-learning intelligent algorithms are suitable not only to evaluate the correlation between gaming addiction patterns but also to predict behavior and prognosis brain response depending on the addiction severity. The current paper aims to enrich the knowledge base of the correlation between gaming activity, decision-making, and brain activation, using Machine Learning (ML) algorithms and advanced neuroimaging techniques. The proposed gaming behavior patterns prediction platform was built inside the experiment environment composed of a Functional Near-Infrared Spectrometer (fNIRS) and the computer version of Iowa Gambling Task (IGT). Thirty healthy participants were hired to perform 100 cards selection while equipped with fNIRS. Thus, accelerated by IGT gaming decision-making process was synchronized with changes of oxy-hemoglobin (HbO) levels in the human brain, averaged, and investigated in the left and the right brain hemispheres as well as different psychosomatic conditions, conditionally divided by blocks with 20 card trials in each: absolute unknown and uncertainty in the first block, “pre-hunch” and “hunch” in the second and third blocks, and conceptuality and risky in the fourth and fifth blocks. The features space was constructed around the HbO signal, split by training and tested in two proportions 70/30 and 80/20, and drove patterns prediction ML-based platform consisted of five mechanics, such as Multiple Regression, Classification and Regression Trees (CART), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest. The algorithm prediction power was validated by the 5- and 10-fold cross-validation method and compared by Root Mean Squared Error (RMSE) and coefficient of determination (R Squared) metrics. Indicators of “the best” fit model, lowest RMSE, and highest R Squared were determined for each block and both brain hemispheres and used to make a conclusion about prediction accuracy: SVM algorithm with RBF kernel, Random Forest, and ANN demonstrated the best accuracy in most cases. Lastly, “best fit” classifiers were applied to the testing dataset and finalized the experiment. Hence, the distribution of gaming score was predicted by five blocks and both brain hemispheres that reflect the decision-making process patterns during gaming. The investigation showed increasing ML algorithm prediction power from IGT block one to five, reflecting an increasing learning effect as a behavioral pattern. Furthermore, performed inside constructed platform simulation could benefit in diagnosing gaming disorders, their patterns, mechanisms, and abnormalities.
Keywords: Functional Near-Infrared Spectroscopy, Machine Learning, Iowa Gambling Task, Cognitive Neuroimaging
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