Disease Modeling Using Evolved Discriminate Function

  • James Cunha Werner
  • Tatiana Kalganova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2610)


Precocious diagnosis increases the survival time and patient quality of life. It is a binary classification, exhaustively studied in the literature. This paper innovates proposing the application of genetic programming to obtain a discriminate function. This function contains the disease dynamics used to classify the patients with as little false negative diagnosis as possible. If its value is greater than zero then it means that the patient is ill, otherwise healthy. A graphical representation is proposed to show the influence of each dataset attribute in the discriminate function. The experiment deals with Breast Cancer and Thrombosis & Collagen diseases diagnosis. The main conclusion is that the discriminate function is able to classify the patient using numerical clinical data, and the graphical representation displays patterns that allow understanding of the model.


Breast Cancer False Negative Discriminate Function Receiver Operating Characteristic Collagen Disease 
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.


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  1. 1.
    Newman, M.; “UK’s cancer death rate is worst in the world”; Metro News, Tuesday, July 2, 2002.Google Scholar
  2. 2.
    Kononenko, I.; “Machine learning for medical diagnosis: history, state of the art and perspective”; Artificial Intelligence in medicine 23:89–109, 2001.CrossRefGoogle Scholar
  3. 3.
    West, D; West, V; “Model selection for a medical diagnostic decision support system: a breast cancer detection case” Artificial Intelligence in medicine 20(2000)183–204.CrossRefGoogle Scholar
  4. 4.
    Setiono, R.; “Extracting rules from pruned neural networks for breast cancer diagnosis” Artificial Intelligence in Medicine 8(1):37–51, 1996.CrossRefGoogle Scholar
  5. 5.
    Setiono, R.; “Generating concise and accurate classification rules for breast cancer diagnosis”; Artificial Intelligence in medicine 18:205–219, 2000CrossRefGoogle Scholar
  6. 6.
    Flach, P.A.; “On the state of art in machine learning: a personal review”; Artificial Intelligence 131:199–222, 2001.zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Joachins, T.; “Tutorial Support Vector Machines” In Internet
  8. 8.
    Land Jr., W.H.; Lo, J.Y.; Velazquez, R.; “Using evolutionary programming to configure support vector machine for the diagnosis of breast cancer”. In Dagli, C.H. et al (Eds) Intelligent engineering systems through artificial neural networks ANNIE’2002, Volume 12, Smart engineering system design, ASME Press, New York, 2002.Google Scholar
  9. 9.
    Pendharkar, P.C.; et al; “Association, statistical, mathematical and neural approaches for mining breast cancer patterns”; Expert Systems with Applications 17:223–232, 1999.CrossRefGoogle Scholar
  10. 10.
    Nauck, D.; Kruse, R.; “Obtaining interpretable fuzzy classification rules from medical data”; Artificial intelligence in medicine 16:149–169, 1999CrossRefGoogle Scholar
  11. 11.
    Freitas, A.A.; “Data mining and knowledge discovery with Evolutionary Algorithms”; Springer 2002.Google Scholar
  12. 12.
    Pena-Reyes, C.A.; Sipper, M.; ℝdA fuzzy-genetic approach to breast cancer diagnosis”; Artificial intelligence in medicine 17:131–155, 1999.CrossRefGoogle Scholar
  13. 13.
    HOLLAND, J.H. “Adaptation in natural and artificial systems: na introductory analysis with applications to biology, control and artificial intelligence.” Cambridge: Cambridge press 1992.Google Scholar
  14. 14.
    GOLDBERG, D.E. “Genetic Algorithms in Search, Optimisation, and Machine Learning.” Reading, Mass.: Addison-Whesley, 1989.Google Scholar
  15. 15.
    CHAMBERS, L.; “The practical handbook of Genetic Algorithms” Chapman & Hall/CRC, 2000.Google Scholar
  16. 16.
    KOZA, J.R. “Genetic programming: On the programming of computers by means of natural selection.” Cambridge,Mass.: MIT Press, 1992.Google Scholar
  17. 17.
    LilGP “Genetic Algorithms Research and Applications Group (GARAGe)”, Michigan State University;
  18. 18.
    Bradley, A.P.; “The use of the area under the ROC curve in the evaluation of machine learning algorithms”; Pattern Recognition, 30(7):1145–1159, 1997.CrossRefGoogle Scholar
  19. 19.
    WDBC Dr. William H. Wolberg, General Surgery Dept.,; W. Nick Street, Computer Sciences Dept.; Olvi L. Mangasarian, Computer Sciences Dept.; University of Wisconsin
  20. 20.
    Werner, J.C.; Fogarty, T.C.; “Severe diseases diagnostics using Genetic Programming.” Intelligent Data Analysis in medicine and pharmacology-IDAMAP2001; September 4th, 2001 London
  21. 21.
    5th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD’01) Challenge on Thrombosis data-Germany/ Freiburg September 3–7, 2001Google Scholar
  22. 22.
    Werner, J.C.; Fogarty, T.C.; “Genetic programming applied to Collagen disease & thrombosis.” in PKDD 2001 Challenge on Thrombosis data-Germany/ Freiburg September 3–7, 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • James Cunha Werner
    • 1
  • Tatiana Kalganova
    • 1
  1. 1.Department of Electronic & Computer EngineeringBrunel UniversityUxbridgeMiddlesex

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