Identifying Environmental and Social Factors Predisposing to Pathological Gambling Combining Standard Logistic Regression and Logic Learning Machine
- 264 Downloads
Identifying potential risk factors for problem gambling (PG) is of primary importance for planning preventive and therapeutic interventions. We illustrate a new approach based on the combination of standard logistic regression and an innovative method of supervised data mining (Logic Learning Machine or LLM). Data were taken from a pilot cross-sectional study to identify subjects with PG behaviour, assessed by two internationally validated scales (SOGS and Lie/Bet). Information was obtained from 251 gamblers recruited in six betting establishments. Data on socio-demographic characteristics, lifestyle and cognitive-related factors, and type, place and frequency of preferred gambling were obtained by a self-administered questionnaire. The following variables associated with PG were identified: instant gratification games, alcohol abuse, cognitive distortion, illegal behaviours and having started gambling with a relative or a friend. Furthermore, the combination of LLM and LR indicated the presence of two different types of PG, namely: (a) daily gamblers, more prone to illegal behaviour, with poor money management skills and who started gambling at an early age, and (b) non-daily gamblers, characterised by superstitious beliefs and a higher preference for immediate reward games. Finally, instant gratification games were strongly associated with the number of games usually played. Studies on gamblers habitually frequently betting shops are rare. The finding of different types of PG by habitual gamblers deserves further analysis in larger studies. Advanced data mining algorithms, like LLM, are powerful tools and potentially useful in identifying risk factors for PG.
KeywordsProblem gambling Logic Learning Machine Logistic regression ROC analysis
Stefano Parodi is a research fellow of the Italian MIUR Flagship project “InterOmics”.
Compliance with Ethical Standards
Conflict of interest
Corrado Dosi is an authorized patron of a betting agency. The other authors have no conflict of interest to declare.
- Abbott, M., Bellringer, M., Garrett, N., & Mundy-McPherson, S. (2012). New Zealand 2012 national gambling study: Gambling harm and problem gambling—Report Number 2. Gambling and Addiction Research Centre, National Institute for Public Health and Mental Health Research, Auckland (New Zealand).Google Scholar
- Cangelosi, D., Muselli, M., Parodi, S., Blengio, F., Becherini, P., Versteeg, R., et al. (2014). Use of attribute driven incremental discretization and Logic Learning Machine to build a prognostic classifier for neuroblastoma patients. BMC Bioinformatics, 15(Suppl 5), S4. doi: 10.1186/1471-2105-15-S5-S4.CrossRefPubMedPubMedCentralGoogle Scholar
- Croce, M., & Nanni, W. (2004). Le dipendenze senza sostanze in Vuoti a perdere “The substances employed without returnable” (Report No: V Caritas sulle nuove poverta`). Feltrinelli: Milano.Google Scholar
- Dowling, N. A., Cowlishaw, S., Jackson, A. C., Merkouris, S. S., Francis, K. L., & Christensen, D. R. (2015). Prevalence of psychiatric co-morbidity in treatment-seeking problem gamblers: A systematic review and meta-analysis. Australian and New Zealand Journal of Psychiatry, 49(6), 519–539. doi: 10.1177/0004867415575774.CrossRefPubMedPubMedCentralGoogle Scholar
- Ferrari, E., & Muselli, M. (2010). Maximizing pattern separation in discretizing continuous features for classification purposes. In Neural Networks (IJCNN), The 2010 International Joint Conference on, 18–23 July 2010.Google Scholar
- Fiasco, M. (2010). Verso l’economia del gioco “Towards the game economy”. Il redattore sociale.Google Scholar
- Goodie, A. S., MacKillop, J., Miller, J. D., Fortune, E. E., Maples, J., Lance, C. E., et al. (2013). Evaluating the South Oaks Gambling Screen with DSM-IV and DSM-5 criteria: Results from a diverse community sample of gamblers. Assessment, 20(5), 523–531. doi: 10.1177/1073191113500522.CrossRefPubMedPubMedCentralGoogle Scholar
- Iliceto, P., D’Antuono, L., Bowden-Jones, H., Giovani, E., Giacolini, T., Candilera, G., et al. (2016). Brain emotion systems, personality, hopelessness, self/other perception, and gambling cognition: A structural equation model. Journal of Gambling Studies, 32(1), 157–169. doi: 10.1007/s10899-015-9543-0.CrossRefPubMedGoogle Scholar
- Kleinbaum, D. G., Kupper, L. L., & Morgenstern, H. (1982). Typology of Observational Study Design. In D. G. Kleinbaum, L. L. Kupper, & H. Morgenstern (Eds.), Epidemiologic Research (pp. 62–95). Belmont: Lifetime Learning Publications.Google Scholar
- Kleinbaum, D. G., Kupper, L. L., Muller, K. E., & Nizam, A. (1998). Applied regression analysis and other multivariable methods. Pacific Grove: Duxbury Press.Google Scholar
- Lavrac, N., Flach, P., & Zupan, B. (1999). Rule Evaluation Measures: A unifying View. Lecture Notes in Computer Science (Vol. 1634, pp. 174–185). Berlin: Springer.Google Scholar
- Lesieur, H. R. (2001). Cluster analysis of types of inpatient pathological gamblers. Dissertation Abstracts International, 62(4-B), 2065.Google Scholar
- Michie, D., Spiegelhalter, D., & Taylor, C. (1994). Machine Learning: Neural and Statistical Classification. New York: Ellis Horwood.Google Scholar
- Muselli, M. (2006). Switching neural networks: A new connectionist model for classification. In B. Apolloni, M. Marinaro, G. Nicosia, & R. Tagliaferri (Eds.), WIRN 2005 and NAIS 2005, Lecture Notes in Computer Science (Vol. 3931, pp. 23–30). Berlin: Springer.Google Scholar
- Parodi, S., Manneschi, C., Verda, D., Ferrari, E., & Muselli, M. (2016). Logic Learning Machine and standard supervised methods for Hodgkin’s lymphoma prognosis using gene expression data and clinical variables. Health Informatics Journal (in press).Google Scholar
- Pepe, M. S. (2003). The statistical evaluation of medical tests for classification and prediction. Oxford: Oxford University Press.Google Scholar
- Public Health Agency of Sweden. (2014). Key results from the Swelogs in-depth study—A report on problem gambling and health. Swelogs Fact Sheet 18—Technical Report. https://www.folkhalsomyndigheten.se/publicerat-material/publikationsarkiv/s/Swelogs-facts-sheet-no-18-2014-Key-results-from-the-Swelogs-in-depth-study. Accessed Jan 19, 2017.
- Williams, R. J., Volberg, R. A., & Stevens, R. M. G. (2012). The population prevalence of problem gambling: Methodological influences, standardized rates, jurisdictional differences, and worldwide trends. Report prepared for the Ontario Problem Gambling Research Centre and the Ontario Ministry of Health and Long Term Care. May 8, 2012.Google Scholar