Soft Computing

, Volume 15, Issue 12, pp 2435–2448 | Cite as

Evolutionary fuzzy rule extraction for subgroup discovery in a psychiatric emergency department

  • C. J. Carmona
  • P. González
  • M. J. del Jesus
  • M. Navío-Acosta
  • L. Jiménez-Trevino


This paper describes the application of evolutionary fuzzy systems for subgroup discovery to a medical problem, the study on the type of patients who tend to visit the psychiatric emergency department in a given period of time of the day. In this problem, the objective is to characterise subgroups of patients according to their time of arrival at the emergency department. To solve this problem, several subgroup discovery algorithms have been applied to determine which of them obtains better results. The multiobjective evolutionary algorithm MESDIF for the extraction of fuzzy rules obtains better results and so it has been used to extract interesting information regarding the rate of admission to the psychiatric emergency department.


Evolutionary fuzzy system Subgroup discovery Fuzzy rules extraction Evolutionary algorithm Psychiatric emergency 



This work was supported by the Spanish Ministry of Education, Social Policy and Sports under projects TIN-2008-06681-C06-02, and by the Andalusian Research Plan under project TIC-3928.


  1. Agrawal R, Imieliski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD international conference on management of data. ACM Press, pp 207–216Google Scholar
  2. Aguilar-Ruiz J, Costa R, Divina F (2004) Knowledge discovery from doctor–patient relationship. In: Proceedings of the ACM symposium on applied computing, vol 1, pp 280–284Google Scholar
  3. Ainon R, Lahsasna A, Wah T (2009) A transparent classification model using a hybrid soft computing method. In: AMS, pp 146–151Google Scholar
  4. Alcalá-Fdez J, Alcalá R, Gacto MJ, Herrera F (2009) Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms. Fuzzy Sets Syst 160(7):905–921zbMATHCrossRefGoogle Scholar
  5. Alhajj R, Kaya M (2008) Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining. J Intell Inform Syst 31(3):243–264CrossRefGoogle Scholar
  6. Atzmueller M, Puppe F (2006) SD-Map—a fast algorithm for exhaustive subgroup discovery. In: Proceedings of the 17th European conference on machine learning and 10th European conference on principles and practice of knowledge discovery in databases, vol 4213. Springer, Berlin, pp 6–17Google Scholar
  7. Atzmueller M, Puppe F, Buscher HP (2004) Towards knowledge-intensive subgroup discovery. In: Proceedings of the Lernen—Wissensentdeckung—Adaptivität—Fachgruppe Maschinelles Lernen, pp 111–117Google Scholar
  8. Atzmueller M, Puppe F, Buscher HP (2005) Profiling examiners using intelligent subgroup mining. In: Workshop on intelligent data analysis in medicine and pharmacology, pp 46–51Google Scholar
  9. Baca-Garcfa E, Perez-Rodriguez M, Basurte-Villamor I, Saiz-Ruiz J, Leiva-Murillo J, Prado-Cumplido MD, Santiago-Mozos R, ArtTs-Rodrfguez A, Leon JD (2006) Using data mining to explore complex clinical decisions: a study of hospitalization after a suicide attempt. J Clin Psychiatry 67(7):1124–1132CrossRefGoogle Scholar
  10. Baca-Garcia E, Perez-Rodriguez M, et al (2008) Patterns of mental health service utilization in a general hospital and outpatient mental health facilities: analysis of 365,262 psychiatric consultations. Eur Arch Psychiatry Clin Neurosci 258(2):117–123CrossRefGoogle Scholar
  11. Botta A, Lazzerini B, Marceloni F, Stefanescu DC (2009) Context adaptation of fuzzy systems through a multi-objective evolutionary approach based on a novel interpretability index. Soft Comput 13(5):437–449CrossRefGoogle Scholar
  12. Bulbena A, Sperry L, Garcia-Ribera C, Merino A, Mateu G, Torrens M, San-Gil J, Cunillera J (2009) Impact of the summer 2003 heat wave on the activity of two psychiatric emergency departments. In: Actas Esp. Psiquiatr., vol 37, pp 158–165Google Scholar
  13. Casillas J, Carse B (2009) Special issue on genetic fuzzy systems: recent developments and future directions. Soft Comput 13(5):417–418CrossRefGoogle Scholar
  14. Chen CH, Hong TP, Tseng VS (2009a) An improved approach to find membership functions and multiple minimum supports in fuzzy data mining. Expert Syst Appl 36(6):10,016–10,024Google Scholar
  15. Chen CH, Hong TP, Tseng VS, Lee CS (2009b) A genetic-fuzzy mining approach for items with multiple minimum supports. Soft Comput 13(5):521–533CrossRefGoogle Scholar
  16. Clark P, Niblett T (1989) The CN2 induction algorithm. Mach Learn 3:261–283Google Scholar
  17. Clercq MD, Lamarre S, Vergouwen H (1998) Emergency psychiatry and mental health policy: an international point of view. Elsevier, AmsterdamGoogle Scholar
  18. Cordón O, Gomide F, Herrera F, Hoffmann F, Magdalena L (2004) Ten years of genetic fuzzy systems. Current framework and new trends. Fuzzy Sets Syst 14:5–31CrossRefGoogle Scholar
  19. Cordón O, Alcalá R, Alcalá-Fdez J, Rojas I (2007) Special issue on genetic fuzzy systems: what’s next? Editorial. IEEE Trans Fuzzy Syst 15(4):533–535CrossRefGoogle Scholar
  20. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Willey, New YorkGoogle Scholar
  21. del Jesus MJ, González P, Herrera F, Mesonero M (2007a) Evolutionary fuzzy rule induction process for subgroup discovery: a case study in marketing. IEEE Trans Fuzzy Syst 15(4):578–592CrossRefGoogle Scholar
  22. del Jesus MJ, González P, Herrera F (2007b) Multiobjective genetic algorithm for extracting subgroup discovery fuzzy rules. In: Proceedings of the IEEE symposium on computational intelligence in multicriteria decision making. IEEE Press, pp 50–57Google Scholar
  23. Drobics M, Botzheim J, Koczy L (2007) Increasing diagnostic accuracy by meta optimization of fuzzy rule bases. In: IEEE international conference on fuzzy systemsGoogle Scholar
  24. Fayyad UM, Irani KB (1993) Multi-interval discretization of continuous-valued attributes for classification learning. In: 13th international joint conference on artificial intelligence, pp 1022–1029Google Scholar
  25. Fayyad UM, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery: an overview. In: Advances in knowledge discovery and data mining. AAAI/MIT Press, pp 1–34Google Scholar
  26. Fogel G (2008) Computational intelligence approaches for pattern discovery in biological systems. Brief Bioinform 9(4):307–316CrossRefGoogle Scholar
  27. Gacto MJ, Alcalá R, Herrera F (2009) Adaptation and application of multi-objective evolutionary algorithms for rule reduction and parameter tuning of fuzzy rule-based systems. Soft Comput 13(5):419–436CrossRefGoogle Scholar
  28. Gamberger D, Lavrac N (2002) Expert-guided subgroup discovery: methodology and application. J Artif Intell Res 17:501–527zbMATHGoogle Scholar
  29. Gamberger D, Lavrac N (2003) Active subgroup mining: a case study in coronary heart disease risk group detection. Artif Intell Med 28(1):27–57CrossRefGoogle Scholar
  30. Gamberger D, Lavrac N, Krstaic A, Krstaic G (2007) Clinical data analysis based on iterative subgroup discovery: experiments in brain ischaemia data analysis. Appl Intell 27(3):205–217CrossRefGoogle Scholar
  31. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Longman Publishing Co, LondonGoogle Scholar
  32. Grosskreutz H, Rueping S (2009) On subgroup discovery in numerical domains. Data Min Knowl Discov 19(2):210–216MathSciNetCrossRefGoogle Scholar
  33. Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM SIGMOD international conference on management of data. ACM Press, pp 1–12Google Scholar
  34. Herrera F (2008) Genetic fuzzy systems: taxomony, current research trends and prospects. Evol Intell 1:27–46CrossRefGoogle Scholar
  35. Hong TP, Chen CH, Wu YL, Lee YC (2006) A GA-based fuzzy mining approach to achieve a trade-off between number of rules and suitability of membership fuctions. Soft Comput 10(11):1091–1101CrossRefGoogle Scholar
  36. Hüllermeier E (2005) Fuzzy methods in machine learning and data mining: status and prospects. Fuzzy Sets Syst 156(3):387–406CrossRefGoogle Scholar
  37. Ishibuchi H (2007) Multiobjective genetic fuzzy systems: review and future research directions. In: IEEE international conference on fuzzy systems, pp 913–918Google Scholar
  38. Jovanoski V, Lavrac N (2001) Classification rule learning with APRIORI-C. In: 10th Portuguese conference on artificial intelligence on progress in artificial intelligence, knowledge extraction, multi-agent systems, logic programming and constraint solving, vol 2258. Springer, Berlin, pp 44–51Google Scholar
  39. Kavsek B, Lavrac N (2006) APRIORI-SD: adapting association rule learning to subgroup discovery. Appl Artif Intell 20:543–583CrossRefGoogle Scholar
  40. Kaya M (2006) Multi-objective genetic algorithm based approaches for mining optimized fuzzy association rules. Soft Comput 10(7):578–586MathSciNetzbMATHCrossRefGoogle Scholar
  41. Kielan K, Kucharska-Pietura K, Warchala A, Konopka M, Pieniazek P, Hartel M (2004) “saba”—application of knowledge and state-of-the-art technologies in the field of psychiatry for development of new diagnostics prevention and therapeutic tools for schizophrenia. In: vol 57(1), pp 152–157Google Scholar
  42. Kloesgen W (1996) Explora: a multipattern and multistrategy discovery assistant. In: Advances in knowledge discovery and data mining. American Association for Artificial Intelligence, pp 249–271Google Scholar
  43. Kralj P, Lavrac N, Gamberger D, Krstaic A (2007) Contrast set mining through subgroup discovery applied to brain ischaemina data. In: 11th Pacific-Asia conference on knowledge discovery and data mining, vol 4426. Springer, Berlin, pp 579–586Google Scholar
  44. Lavrac N, Flach PA, Zupan B (1999) Rule evaluation measures: a unifying view. In: Proceedings of the 9th international workshop on inductive logic programming, vol 1634. Springer, Berlin, pp 174–185Google Scholar
  45. Lavrac N, Flach P, Kavsek B, Todorovski L (2002) Rule induction for subgroup discovery with CN2-SD. In: Proceedings of the 2nd international workshop on integration and collaboration aspects of data mining, decision support and meta-learning, pp 77–87Google Scholar
  46. Lavrac N, Cestnik B, Gamberger D, Flach PA (2004a) Decision support through subgroup discovery: three case studies and the lessons learned. Mach Learn 57(1–2):115–143zbMATHCrossRefGoogle Scholar
  47. Lavrac N, Kavsek B, Flach PA, Todorovski L (2004b) Subgroup Discovery with CN2-SD. J Mach Learn Res 5:153–188MathSciNetGoogle Scholar
  48. López B, Barrera V, Meléndez J, Pous C, Brunet J, Sanz J (2009) Subgroup discovery for weight learning in breast cancer diagnosis. In: Proceedings of the 12th conference on artificial intelligence in medicine, vol 5651. LNAI, pp 360–364Google Scholar
  49. Mantzaris D, Anastassopoulos G, Iliadis L, Adamopoulos A (2009) An evolutionary technique for medical diagnostic risk factors selection. IFIP Int Federation Inform Process 195–203Google Scholar
  50. Masuda G, Sakamoto N, Yamamoto R (2002) A framework for dynamic evidence based medicine using data mining. In: Proceedings of the IEEE symposium on computer-based medical systems, pp 117–122Google Scholar
  51. Michie D, Spiegelhalter DJ, Tayloy CC (1994) Machine learning. Ellis HorwoodGoogle Scholar
  52. Mueller M, Rosales R, Steck H, Krishnan S, Rao B, Kramer S (2009) Subgroup discovery for test selection: a novel approach and its application to breast cancer diagnosis. In: Proceedings of the 8th international symposium on intelligent data analysis, vol 5772. Springer, Berlin, pp 119–130Google Scholar
  53. Nannings B, Bosnian RJ, Abu-Hanna A (2009) A subgroup discovery approach for scrutinizing blood glucose management guidelines by the identification of hyperglycemia determinants in ICU patients. Methods Inform Med 47(6):480–488Google Scholar
  54. Papageorgiou E, Papandrianos N, Apostolopoulos D, Vassilakos P (2008) Fuzzy cognitive map based decision support system for thyroid diagnosis management. In: IEEE international conference on fuzzy systems, pp 1204–1211Google Scholar
  55. Romero C, González P, Ventura S, del Jesus MJ, Herrera F (2009) Evolutionary algorithm for subgroup discovery in e-learning: a practical application using Moodle data. Expert Syst Appl 36:1632–1644CrossRefGoogle Scholar
  56. ValdTs J, Barton A, AS Haqqani A (2008) Analysis of mass spectrometry data of cerebral stroke samples: an evolutionary computation approach to resolve and quantify peptide peaks. Genet Program Evol Mach 9(3):257–274CrossRefGoogle Scholar
  57. Wrobel S (1997) An algorithm for multi-relational discovery of subgroups. In: Proceedings of the 1st European symposium on principles of data mining and knowledge discovery, vol 1263. Springer, Berlin, pp 78–87Google Scholar
  58. Yardimci A (2009) Soft computing in medicine. Appl Soft Comput J 9(3):1029–1043CrossRefGoogle Scholar
  59. Yu L, Wu C, Yeh J, Jang F (2008) HAL-based evolutionary inference for pattern induction from psychiatry web resources. IEEE Trans Evol Comput 12(2):160–170CrossRefGoogle Scholar
  60. Zadeh LA (1975) The concept of a linguistic variable and its applications to approximate reasoning. Parts I, II, III. Information Science 8–9:199–249,301–357,43–80Google Scholar
  61. Zelezny F, Lavrac N (2006) Propositionalization-based relational subgroup discovery with RSD. Mach Learn 62:33–63CrossRefGoogle Scholar
  62. Zitzler E, Laumanns M, Thiele L (2002) SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In: International congress on evolutionary methods for design optimization and control with applications to industrial problems, pp 95–100Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • C. J. Carmona
    • 1
  • P. González
    • 1
  • M. J. del Jesus
    • 1
  • M. Navío-Acosta
    • 2
  • L. Jiménez-Trevino
    • 3
  1. 1.Department of Computer ScienceUniversity of JaenJaenSpain
  2. 2.Hospital Universitario 12 de Octubre, CIBERSAMMadridSpain
  3. 3.Department of PsychiatryUniversity of Oviedo, CIBERSAMOviedoSpain

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