Personality biomarkers of pathological gambling: A machine learning study

Antonio Cerasa*, Danilo Lofaro, Paolo Cavedini, Iolanda Martino, Antonella Bruni, Alessia Sarica, Domenico Mauro, Giuseppe Merante, Ilaria Rossomanno, Maria Rizzuto, Antonio Palmacci, Benedetta Aquino, Pasquale De Fazio, Giampaolo R. Perna, Elena Vanni, Giuseppe Olivadese, Domenico Conforti, Gennarina Arabia, Aldo Quattrone

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Web of Science)

Abstract

Background: The application of artificial intelligence to extract predictors of Gambling disorder (GD) is a new field of study. A plethora of studies have suggested that maladaptive personality dispositions may serve as risk factors for GD. New method: Here, we used Classification and Regression Trees algorithm to identify multivariate predictive patterns of personality profiles that could identify GD patients from healthy controls at an individual level. Forty psychiatric patients, recruited from specialized gambling clinics, without any additional comorbidity and 160 matched healthy controls completed the Five-Factor model of personality as measured by the NEO-PI-R, which were used to build the classification model. Results: Classification algorithm was able to discriminate individuals with GD from controls with an AUC of 77.3% (95% CI 0.65-0.88, p < 0.0001). A multidimensional construct of traits including sub-facets of openness, neuroticism and conscientiousness was employed by algorithm for classification detection. Comparison with existing method(s): To the best of our knowledge, this is the first study that combines behavioral data with machine learning approach useful to extract multidimensional features characterizing GD realm. Conclusion: Our study provides a proof-of-concept demonstrating the potential of the proposed approach for GD diagnosis. The multivariate combination of personality facets characterizing individuals with GD can potentially be used to assess subjects' vulnerability in clinical setting. (C) 2017 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)7-14
Number of pages8
JournalJournal of Neuroscience Methods
Volume294
DOIs
Publication statusPublished - 15 Jan 2018

Keywords

  • Pathological gambling
  • Personality profile
  • Machine learning
  • Five-factor model
  • Conscientiousness
  • Openness
  • Neuroticism
  • 5-FACTOR MODEL
  • PARKINSONS-DISEASE
  • DIAGNOSIS
  • TRAITS
  • DSM-5
  • CLASSIFICATION
  • DISORDERS
  • SUPPORT
  • RISK
  • METAANALYSIS

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