Personality biomarkers of pathological gambling: A machine learning study

Publication date: 15 January 2018
Source:Journal of Neuroscience Methods, Volume 294
Author(s): 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
BackgroundThe 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 methodHere, 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.ResultsClassification 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.ConclusionOur 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.



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