Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/110432
Title: | Identifying compulsive gamblers using Bayesian networks |
Authors: | Caruana, Mark Anthony Farrugia, Christian A. |
Keywords: | Compulsive gambling -- Prevention Neural networks (Computer science) Bayesian statistical decision theory Graph theory -- Data processing Machine learning -- Statistics |
Issue Date: | 2023 |
Publisher: | EUROSIS |
Citation: | Caruana, M. A., & Farrugia, C. A. (2023, May). Identifying compulsive gamblers using Bayesian networks. ISC’2023 Conference. University of Malta, Valletta Campus. 1-5. |
Abstract: | The grave consequences suffered by online problem gamblers has led to a growing interest in responsible gambling measures with the intention of preventing players from reaching such a vulnerable state. The focus of this dissertation is to apply statistical machine learning techniques to predict whether a player is most likely a problem gambler or not, whilst identifying which variables were deemed useful predictors of problem gambling. Bayesian networks are implemented on a data set containing historical data obtained from a local medium-sized Malta Gambling Authority (MGA) gambling operator. The models are tested based on a number of goodness of fit measures such as the predictive accuracy and Area Under the Curve (AUC). |
URI: | https://www.eurosis.org/cms/index.php https://www.um.edu.mt/library/oar/handle/123456789/110432 |
Appears in Collections: | Scholarly Works - FacSciSOR |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Identifying compulsive gamblers using Bayesian networks 2023.pdf Restricted Access | 455.89 kB | Adobe PDF | View/Open Request a copy |
Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.