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A Tool for Computer-Aided Diagnosis of Psychological Disorders Based on the MMPI Test: An Overview

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 606)

Abstract

The goal of the paper is to summarize our research, conducted for over the last five years, on computer-aided diagnosis of patients screened with the MMPI (Minnesota Multiphasic Personality Inventory) test. The MMPI test delivers psychometric data, in the form of the so-called profiles (thirteen descriptive attributes corresponding to three validity and ten clinical scales), enabling us to diagnose selected psychological disorders. The notable effect of conducted research is a new computer tool, called Copernicus, aiding diagnosis of psychological disorders based on MMPI profiles. The paper is focused on outlying the functionality of the created tool.

Keywords

Computer Tool Minnesota Multiphasic Personality Inventory Decision Class Fuzzy Decision Tree MMPI Profile 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
  2. 2.
    Bazan, J.G., Szczuka, M.S.: The rough set exploration system. In: Peters, J., Skowron, A. (eds.) Transactions on Rough Sets III. LNAI, vol. 3400, pp. 37–56. Springer, Berlin (2005)CrossRefGoogle Scholar
  3. 3.
    Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Chapman & Hall, Boca Raton (1993)Google Scholar
  4. 4.
    Choynowski, M.: Multiphasic Personality Inventory (in Polish). Polish Academy of Sciences, Psychometry Laboratory, Warsaw (1964)Google Scholar
  5. 5.
    Cios, K., Pedrycz, W., Swiniarski, R., Kurgan, L.: Data Mining. A Knowledge Discovery Approach. Springer, New York (2007)zbMATHGoogle Scholar
  6. 6.
    Duch, W., Kucharski, T., Gomuła, J., Adamczak, R.: Machine Learning Methods in Analysis of Psychometric Data. Application to Multiphasic Personality Inventory MMPI-WISKAD (in Polish). Toruń (1999)Google Scholar
  7. 7.
    Gomuła, J., Paja, W., Pancerz, K., Mroczek, T., Wrzesień, M.: Experiments with hybridization and optimization of the rules knowledge base for classification of MMPI profiles. In: Perner, P. (ed.) Advances on Data Mining: Applications and Theoretical Aspects. LNAI, vol. 6870, pp. 121–133. Springer, Berlin (2011)CrossRefGoogle Scholar
  8. 8.
    Gomuła, J., Paja, W., Pancerz, K., Szkoła: A preliminary attempt to rules generation for mental disorders. In: Proceedings of the International Conference on Human System Interaction (HSI 2010). Rzeszów, Poland (2010)Google Scholar
  9. 9.
    Gomuła, J., Paja, W., Pancerz, K., Szkoła, J.: Rule-based analysis of MMPI data using the Copernicus system. In: Hippe, Z., Kulikowski, J., Mroczek, T. (eds.) Human-Computer Systems Interaction. Backgrounds and Applications 2. Part II. Advances in Intelligent and Soft Computing, vol. 99, pp. 191–203. Springer, Berlin (2012)Google Scholar
  10. 10.
    Gomuła, J., Pancerz, K., Szkoła, J.: Analysis of MMPI profiles of patients with mental disorders—the first unveil af a new computer tool. In: Grzech, A., Świa̧tek, P., Brzostowski, K. (eds.) Applications of Systems Science, pp. 297–306. Academic Publishing House EXIT, Warsaw, Poland (2010)Google Scholar
  11. 11.
    Gomuła, J., Pancerz, K., Szkoła, J.: Classification of MMPI profiles of patients with mental disorders—experiments with attribute reduction and extension. In: Yu, J., et al. (eds.) Rough Set and Knowledge Technology. LNAI, vol. 6401, pp. 411–418. Springer, Berlin (2010)CrossRefGoogle Scholar
  12. 12.
    Gomuła, J., Pancerz, K., Szkoła, J.: Computer-aided diagnosis of patients with mental disorders using the Copernicus system. In: Proceedings of the International Conference on Human System Interaction (HSI 2011). Yokohama, Japan (2011)Google Scholar
  13. 13.
    Gomuła, J., Pancerz, K., Szkoła, J.: Copernicus—an expert system supporting differential diagnosis of patients examined using the MMPI test: an index-rule approach. In: Traver, V., Fred, A., Filipe, J., Gamboa, H. (eds.) Proceedings of the International Conference on Health Informatics (HEALTHINF 2011), pp. 323–328. Italy, Rome (2011)Google Scholar
  14. 14.
    Gomuła, J., Pancerz, K., Szkoła, J.: Rule-based classification of MMPI data of patients with mental disorders: Experiments with basic and extended profiles. Int. J. Comput. Intell. Syst. 4(5), 1022–1031 (2011)Google Scholar
  15. 15.
    Grzymala-Busse, J.: A new version of the rule induction system LERS. Fundam. Inf. 31, 27–39 (1997)zbMATHGoogle Scholar
  16. 16.
    Grzymala-Busse, J., Hippe, Z., Mroczek, T.: Deriving belief networks and belief rules from data: a progress report. In: Peters, J., Skowron, A. (eds.) Transactions on Rough Sets VII. Lecture Notes in Computer Science, vol. 4400, pp. 53–69. Springer, Berlin (2007)CrossRefGoogle Scholar
  17. 17.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11, 10–18 (2009)Google Scholar
  18. 18.
    Hatcher, W.E.: Automated classification of MMPI profiles into psychotic, neurotic or personality disorder types. Comput Program. Biomed. 8(1), 77–80 (1978)CrossRefGoogle Scholar
  19. 19.
    Hill, T., Lewicki, P.: Statistics Methods and Applications. StatSoft, Tulsa (2007)Google Scholar
  20. 20.
    Hippe, Z.: Machine learning: a promising strategy for business information processing? In: Abramowicz, W. (ed.) Business Information Systems, pp. 603–622. Academy of Economics Editorial Office, Poznan (1997)Google Scholar
  21. 21.
    Jachyra, D., Gomuła, J., Pancerz, K.: Rule-based classification of patients screened with the MMPI test in the Copernicus system. In: Iantovics, B., Kountchev, R. (eds.) Advanced Intelligent Computational Technologies and Decision Support Systems. Studies in Computational Intelligence, vol. 486, pp. 31–42. Springer International Publishing (2014)Google Scholar
  22. 22.
    Jachyra, D., Pancerz, K., Gomuła, J.: Classification of MMPI profiles using decision trees. In: Szczuka, M., Czaja, L., Skowron, A., Kacprzak, M. (eds.) Proceedings of the Workshop on Concurrency, Specification and Programming (CS&P 2011), pp. 397–407. Pułtusk, Poland (2011)Google Scholar
  23. 23.
    Jachyra, D., Pancerz, K., Gomuła, J.: Multiway classification of MMPI profiles. In: Zaitseva, E., Levashenko, V. (eds.) Proceedings of the Ninth International Conference on Digital Technologies (DT 2013), pp. 119–127. Zilina, Slovakia (2013)Google Scholar
  24. 24.
    Knap, M.: Research on new algorithms for decision trees generation (in Polish). Ph.D. thesis, AGH University of Science and Technology, Krakow (2009)Google Scholar
  25. 25.
    Lachar, D.: The MMPI: Clinical Assessment and Automated Interpretations. Western Psychological Services, Fate Angeles (1974)Google Scholar
  26. 26.
    Levashenko, V., Zaitseva, E., Pancerz, K., Gomuła, J.: Fuzzy decision tree based classification of psychometric data. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Position Papers of the 2014 Federated Conference on Computer Science and Information Systems. Annals of Computer Science and Information Systems, vol. 3, pp. 37–41. PTI, Warsaw (2014)Google Scholar
  27. 27.
    Levashenko, V., Zaitseva, E., Puuronen, S.: Fuzzy classifier based on fuzzy decision tree. In: Proceedings of the International Conference on Computer as a Tool (EUROCON 2007), pp. 823–827. IEEE (2007)Google Scholar
  28. 28.
    Mich, O., Burda, A., Pancerz, K., Gomuła, J.: The knowledge base for computer-aided diagnosis of mental disorders based on psychometric tests. In: Proceedings of the 10th International Conference on Digital Technologies (DT’2014), pp. 266–272. Zilina, Slovakia (2014)Google Scholar
  29. 29.
    Nichols, D.: Essentials of MMPI-2 Assessment. Wiley, New York (2001)Google Scholar
  30. 30.
    Paja, W., Hippe, Z.: Feasibility studies of quality of knowledge mined from multiple secondary sources. I. Implementation of generic operations. In: Klopotek, M., Wierzchon, S., Trojanowski, K. (eds.) Intelligent Information Processing and Web Mining. Advances in Intelligent and Soft Computing, vol. 31, pp. 461–465. Springer, Berlin (2005)Google Scholar
  31. 31.
    Pancerz, K., Lewicki, A., Tadeusiewicz, R., Gomua, J.: Ant based clustering of MMPI data—an experimental study. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds.) Rough Sets and Knowledge Technology. Lecture Notes in Computer Science, vol. 6954, pp. 366–375. Springer, Berlin (2011)CrossRefGoogle Scholar
  32. 32.
    Pancheri, P., De Fidio, D.: Dal minnesota multiphasic personality inventory al Panda: il MMPI-2 automatico. In: Technical ReportGoogle Scholar
  33. 33.
    Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Dordrecht (1991)zbMATHGoogle Scholar
  34. 34.
    Płużek, Z.: Value of the WISKAD-MMPI test for nosological differential diagnosis (in Polish). The Catholic University of Lublin (1971)Google Scholar
  35. 35.
    Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1992)Google Scholar
  36. 36.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.University of Management and AdministrationZamośćPoland
  2. 2.University of Information Technology and ManagementRzeszówPoland
  3. 3.Medan FoundationThe Andropause InstituteWarsawPoland
  4. 4.Cardinal Stefan Wyszyński UniversityWarsawPoland

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