Identifying Environmental and Social Factors Predisposing to Pathological Gambling Combining Standard Logistic Regression and Logic Learning Machine

  • Stefano Parodi
  • Corrado Dosi
  • Antonella Zambon
  • Enrico Ferrari
  • Marco Muselli
Original Paper

DOI: 10.1007/s10899-017-9679-1

Cite this article as:
Parodi, S., Dosi, C., Zambon, A. et al. J Gambl Stud (2017). doi:10.1007/s10899-017-9679-1


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.


Problem gambling Logic Learning Machine Logistic regression ROC analysis 

Supplementary material

10899_2017_9679_MOESM1_ESM.docx (41 kb)
Supplementary material 1 (DOCX 40 kb)

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Institute of Electronics, Computer and Telecommunication EngineeringNational Research Council of ItalyGenoaItaly
  2. 2.Agenzia ScommessePunto SNAIGenoaItaly
  3. 3.Department of Statistics and Quantitative Methods, Division of Biostatistics, Epidemiology and Public HealthUniversity of Milano-BicoccaMilanItaly
  4. 4.IMPARA SrlGenoaItaly

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