Skip to main content
SpringerLink
Log in

The Five Factor Model of Personality and Evaluation of Drug Consumption Risk

  • Conference paper
  • First Online:
Data Science

Abstract

The problem of evaluating an individual’s risk of drug consumption and misuse is highly important and novel. An online survey methodology was employed to collect data including personality traits (NEO-FFI-R), impulsivity (BIS-11), sensation seeking (ImpSS), and demographic information. The data set contained information on the consumption of 18 central nervous system psychoactive drugs. Correlation analysis using a relative information gain model demonstrates the existence of a group of drugs (amphetamines, cannabis, cocaine, ecstasy, legal highs, LSD, and magic mushrooms) with strongly correlated consumption. An exhaustive search was performed to select the most effective subset of input features and data mining methods to classify users and non-users for each drug. A number of classification methods were employed (decision tree, random forest, k-nearest neighbours, linear discriminant analysis, Gaussian mixture, probability density function estimation, logistic regression, and naïve Bayes) and the most effective method selected for each drug. The quality of classification was surprisingly high. The best results with sensitivity and specificity being greater than 75% were achieved for cannabis, crack, ecstasy, legal highs, LSD, and volatile substance abuse. Sensitivity and specificity greater than 70% were achieved for amphetamines, amyl nitrite, benzodiazepines, chocolate, caffeine, heroin, ketamine, methadone, and nicotine. The poorest result was obtained for prediction of alcohol consumption.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Arlot, S., Celisse, A.: A survey of cross-validation procedures for model selection. Stat. Surv. 4, 40–79 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  2. Beaglehole, R., Bonita, R., Horton, R., Adams, C., Alleyne, G., Asaria, P., et al.: Priority actions for the non-communicable disease crisis. Lancet 377(9775), 1438–1447 (2011)

    Article  Google Scholar 

  3. Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Royal Stat. Soc. 57(1), 289–300 (1995)

    MathSciNet  MATH  Google Scholar 

  4. Biau, G.: Analysis of a random forests model. J. Mach. Learn. Res. 13(1), 1063–1095 (2012)

    MathSciNet  MATH  Google Scholar 

  5. Bogg, T., Roberts, B.W.:. Conscientiousness and health-related behaviors: a meta-analysis of the leading behavioral contributors to mortality. Psychol. Bull. 130(6), 887–919 (2004)

    Article  Google Scholar 

  6. Bulut, F., Bucak, İ.Ö.: An urgent precaution system to detect students at risk of substance abuse through classification algorithms. Turk. J. Electr. Eng. Comput. Sci. 22(3), 690–707 (2014)

    Article  Google Scholar 

  7. Clarkson, K.L.: Nearest-neighbor searching and metric space dimensions. In: Shakhnarovich, G., Darrell, T., Indyk, P. (eds.) Nearest-Neighbor Methods for Learning and Vision: Theory and Practice, pp. 15–59. MIT, Cambridge (2005)

    Google Scholar 

  8. Cleveland, M.J., Feinberg, M.E., Bontempo, D.E., Greenberg, M.T.: The role of risk and protective factors in substance use across adolescence. J. Adolesc. Health 43(2), 157–164 (2008)

    Article  Google Scholar 

  9. Costa, P.T., MacCrae, R.R.: Revised NEO-Personality Inventory (NEO-PI-R) and the NEO-Five Factor Inventory (NEO-FFI): Personality manual. Psychological Assessment Resources, Odessa, FL (1992)

    Google Scholar 

  10. Dinov, I.D.: Expectation maximization and mixture modeling tutorial. UCLA, Statistics Online Computational Resource (2008). http://escholarship.org/uc/item/1rb7097

    Google Scholar 

  11. Egan, V., Deary, I., Austin, E.: The NEO-FFI: emerging British norms and an item-level analysis suggest N, A and C are more reliable than O and E. Personal. Individ. Differ. 29(5), 907–920 (2000)

    Google Scholar 

  12. Fehrman, E., Muhammad, A.K., Mirkes, E.M., Egan, V., Gorban, A.N.: The five factor model of personality and evaluation of drug consumption risk. arXiv preprint arXiv:1506.06297

    Google Scholar 

  13. Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugenics 7(2), 179–188 (1936)

    Article  Google Scholar 

  14. Gorban, A.N., Zinovyev, A.Y.: Principal graphs and manifolds. In Olivas, E.S., Guerrero, J.D.M., Sober, M.M., Benedito, J.R.M., López, A.J.S. (eds.) Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, pp 28–59. IGI Global, Hershey, NY (2009)

    Google Scholar 

  15. Gorban, A.N., Zinovyev, A.Y.: Principal manifolds and graphs in practice: from molecular biology to dynamical systems. Int. J. Neural Syst. 20(3), 219–232 (2010)

    Article  Google Scholar 

  16. Gorban, A.N., Kégl, B., Wunsch, D.C., Zinovyev, A.Y. (eds.): Principal Manifolds for Data Visualisation and Dimension Reduction. Lecture Notes in Computer Science and Engineering, vol. 58. Springer, Berlin, Heidelberg (2008)

    Google Scholar 

  17. Gujarati, D.N.: Basic Econometrics, 4th edn. McGraw-Hill, New York (2003)

    Google Scholar 

  18. Guttman, L.: Some necessary conditions for common-factor analysis. Psychometrika 19(2), 149–161 (1954)

    Article  MathSciNet  MATH  Google Scholar 

  19. Hastie, T., Tibshirani, R.: Discriminant adaptive nearest neighbor classification. IEEE Trans. Pattern Anal. Mach. Intell. 18(6), 607–616 (1996)

    Article  Google Scholar 

  20. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, New York (2009)

    Book  MATH  Google Scholar 

  21. Hoare, J., Moon, D.: Drug misuse declared: findings from the 2009/10 British Crime Survey Home Office Statistical Bulletin 13/10 (2010)

    Google Scholar 

  22. Hosmer, D.W. Jr., Lemeshow, S.: Applied Logistic Regression. Wiley, New York (2004)

    MATH  Google Scholar 

  23. Kaiser, H.F.: The application of electronic computers to factor analysis. Educ. Psychol. Meas. 20, 141–151 (1960)

    Article  Google Scholar 

  24. Kopstein, A.N., Crum, R.M., Celentano, D.D., Martin, S.S.: Sensation seeking needs among 8th and 11th graders: characteristics associated with cigarette and marijuana use. Drug Alcohol Depend. 62(3), 195–203 (2001)

    Article  Google Scholar 

  25. Koren, Y., Carmel, L.: Robust linear dimensionality reduction. IEEE Trans. Vis. Comput. Graph. 10(4), 459–470 (2004)

    Article  Google Scholar 

  26. Lee, S.Y., Poon, W.Y., Bentler, P.M.: A two-stage estimation of structural equation models with continuous and polytomous variables. Br. J. Math. Stat. Psychol. 48(2), 339–358 (1995)

    Article  MATH  Google Scholar 

  27. Li, Q., Racine, J.S.: Nonparametric Econometrics: Theory and Practice. Princeton University Press, Princeton, NJ (2007)

    MATH  Google Scholar 

  28. Linting, M., van der Kooij, A.: Nonlinear principal components analysis with CATPCA: a tutorial. J. Pers. Assess. 94(1), 12–25 (2012)

    Article  Google Scholar 

  29. McCabe, G.P.: Principal variables. Technometrics 26(2), 137–144 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  30. McCrae, R.R., Costa, P.T.: A contemplated revision of the NEO Five-Factor Inventory. Personal. Individ. Differ. 36(3), 587–596 (2004)

    Article  Google Scholar 

  31. Mirkes, E.M., Alexandrakis, I., Slater, K., Tuli, R., Gorban, A.N.: Computational diagnosis and risk evaluation for canine lymphoma. Comput. Biol. Med. 53, 279–290 (2014)

    Article  Google Scholar 

  32. Mirkes, E.M., Alexandrakis, I., Slater, K., Tuli, R., Gorban, A.N.: Computational diagnosis of canine lymphoma. J. Phys. Conf. Ser. 490(1), 012135 (2014). http://stacks.iop.org/1742--6596/490/i=1/a=012135

    Article  Google Scholar 

  33. Mitchell, T.M.: Machine learning. 1997. Burr Ridge, IL: McGraw Hill 45 (1997).

    Google Scholar 

  34. Naikal, N., Yang, A.Y., Sastry, S.S.: Informative feature selection for object recognition via sparse PCA. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 818–825. IEEE, New York (2011)

    Google Scholar 

  35. Pearson, K.: On lines and planes of closest fit to systems of points in space. Philos. Mag. 2(6), 559–572 (1901)

    Article  MATH  Google Scholar 

  36. Quinlan, J.R.: Simplifying decision trees. Int. J. Man Mach. Stud. 27(3), 221–234 (1987)

    Article  Google Scholar 

  37. Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, Prentice Hall, NJ (1995)

    MATH  Google Scholar 

  38. Scott, D.W.: Multivariate Density Estimation: Theory, Practice and Visualization. Wiley, New York (1992)

    Book  MATH  Google Scholar 

  39. Sofeikov, K.I., Tyukin, I.Y., Gorban, A.N., Mirkes, E.M., Prokhorov, D.V., Romanenko, I.V.: Learning optimization for decision tree classification of non-categorical data with information gain impurity criterion. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 3548–3555. IEEE, New York (2014)

    Google Scholar 

  40. Stanford, M.S., Mathias, C.W., Dougherty, D.M., Lake, S.L., Anderson, N.E., Patton, J.H.: Fifty years of the Barratt impulsiveness scale: an update and review. Personal. Individ. Differ. 47(5), 385–395 (2009)

    Article  Google Scholar 

  41. Terracciano, A., Lóckenhoff, C.E., Crum, R.M., Bienvenu, O.J., Costa, P.T.: Five factor model personality profiles of drug users. BMC Psych. 8(1), 22 (2008)

    Article  Google Scholar 

  42. Valeroa, S., Daigre, C., Rodríguz-Cintas, L., Barral C., Gomà-i-Freixanet, M., Ferrer, M., Casasa, M., Roncero, C.R.: Neuroticism and impulsivity: Their hierarchical organization in the personality characterization of drug-dependent patients from a decision tree learning perspective. Compr. Psychiatry 55(5), 1227–1233 (2014)

    Article  Google Scholar 

  43. Ventura, C.A., de Souza, J., Hayashida, M., Ferreira, P.S.: Risk factors for involvement with illegal drugs: opinion of family members or significant others. J. Subst. Use 20(2), 136–142 (2014)

    Article  Google Scholar 

  44. Yasnitskiy, L., Gratsilev, V., Kulyashova, J., Cherepanov, F.: Possibilities of artificial intellect in detection of predisposition to drug addiction. Perm University Herald Series “Philosophy Psychology Sociology” 1(21), 61–73 (2015)

    Google Scholar 

  45. Zuckerman, M.: Behavioral Expressions and Biosocial Bases of Sensation Seeking. Cambridge University Press, New York (1994)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Men’s Personality Disorder and National Women’s Directorate, Rampton Hospital, Retford, Nottinghamshire, DN22 0PD, UK

    Elaine Fehrman

  2. Department of Mathematics, University of Leicester, Leicester, LE1 7RH, UK

    Awaz K. Muhammad, Evgeny M. Mirkes & Alexander N. Gorban

  3. Department of Psychiatry and Applied Psychology, University of Nottingham, Nottingham, NG8 1BB, UK

    Vincent Egan

Authors
  1. Elaine Fehrman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Awaz K. Muhammad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Evgeny M. Mirkes
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vincent Egan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alexander N. Gorban
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elaine Fehrman .

Editor information

Editors and Affiliations

  1. Department of Political Sciences, University of Naples Federico II, Napoli, Italy

    Francesco Palumbo

  2. Department of Statistical Sciences Paolo Fortunati, Alma Mater Studiorum, University of Bologna, Bologna, Italy

    Angela Montanari

  3. Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy

    Maurizio Vichi

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Fehrman, E., Muhammad, A.K., Mirkes, E.M., Egan, V., Gorban, A.N. (2017). The Five Factor Model of Personality and Evaluation of Drug Consumption Risk. In: Palumbo, F., Montanari, A., Vichi, M. (eds) Data Science . Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-55723-6_18

Download citation

Publish with us

Policies and ethics

Search

Navigation