Genetic Algorithms to Simplify Prognosis of Endocarditis

  • Leticia Curiel
  • Bruno Baruque
  • Carlos Dueñas
  • Emilio Corchado
  • Cristina Pérez-Tárrago
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6936)


This ongoing interdisciplinary research is based on the application of genetic algorithms to simplify the process of predicting the mortality of a critical illness called endocarditis. The goal is to determine the most relevant features (symptoms) of patients (samples) observed by doctors to predict the possible mortality once the patient is in treatment of bacterial endocarditis. This can help doctors to prognose the illness in early stages; by helping them to identify in advance possible solutions in order to aid the patient recover faster. The results obtained using a real data set, show that using only the features selected by employing a genetic algorithm from each patient’s case can predict with a quite high accuracy the most probable evolution of the patient.


Genetic Algorithm Support Vector Machine Infective Endocarditis Feature Selection Algorithm Imperialist Competitive Algorithm 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Liu, H., Yu, L.: Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Educational Activities Department 17(4), 491–502 (2005)MathSciNetGoogle Scholar
  2. 2.
    Lorena, A.C., Ponce, A.C.: Evolutionary design of code-matrices for multiclass problems. In: Soft Computing for Knowledge Discovery and Data Mining, pp. 153–184. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Berlanga, F.J., Rivera, A.J., Jesus, M.J., Herrera, F.: GP-COACH: Genetic Programming-based learning of Compact and Accurate fuzzy rule-based classification systems for High-dimensional problems. Information Science 180(8), 1183–1200 (2010)CrossRefGoogle Scholar
  4. 4.
    Chang, C.-D., Wang, C.-C., Jiang, B.C.: Using data mining techniques for multi-diseases prediction modeling of hypertension and hyperlipidemia by common risk factors. Expert Systems with Applications 38(5), 5507–5513 (2011)CrossRefGoogle Scholar
  5. 5.
    Baruque, B., Corchado, E., Mata, A., Corchado, J.M.: A forecasting solution to the oil spill problem based on a hybrid intelligent system. Information Sciences, Special Issue on Intelligent Distributed Information Systems 180(10), 2029–2043 (2010)Google Scholar
  6. 6.
    Sedano, J., Curiel, L., Corchado, E., de la Cal, E., Villar, J.R.: A Soft Computing Based Method for Detecting Lifetime Building Thermal Insulation Failures. Integrated Computer-Aided Engineering 17(2), 103–115 (2010)Google Scholar
  7. 7.
    Plicht, B., Erbel, R.: Diagnosis and treatment of infective endocarditis. Current ESC guidelines. HERZ 35(8), 542–548 (2010)CrossRefGoogle Scholar
  8. 8.
    Plicht, B., Janosi, R.A., Buck, T., Erbel, R.: Infective endocarditis as cardiovascular emergency. HERZ 51(8), 987–994 (2010)Google Scholar
  9. 9.
    Holland, J.H.: Adaptation in natural and artificial systems. MIT Press, Cambridge (1992)Google Scholar
  10. 10.
    Goldberg, D.E.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)Google Scholar
  11. 11.
    Niknam, T., Fard, E.T., Pourjafarian, N., Rousta, A.: An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering. In: Engineering Applications of Artificial Intelligence, vol. 24, pp. 306–317. Pergamon-Elsevier Science Ltd. (2011)Google Scholar
  12. 12.
    Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97, 273–324 (1997)CrossRefzbMATHGoogle Scholar
  13. 13.
    Vapnik, V.: Statistical Learning Theory. Springer, New York (1998)zbMATHGoogle Scholar
  14. 14.
    Burges, C.J.C.: A tutorial on Support Vector Machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)CrossRefGoogle Scholar
  15. 15.
    Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1(1), 81–106 (1986)Google Scholar
  16. 16.
    Rish, I.: An empirical study of the naive Bayes classifier. In: Proceedings of IJCAI-2001 Workshop on Empirical Methods in AI In International Joint Conference on Artificial Intelligence, pp. 41–46 (2001)Google Scholar
  17. 17.
    Bayes, T.: An Essay towards solving a Problem in the Doctrine of Chances. Philosophical Transactions of the Royal Society of London 53(2), 370–418 (1763)Google Scholar
  18. 18.
    Larrañaga, P., Inza, I., Martinez, A.P.: Bayesian classifiers based on kernel density estimation. International Journal of Approximate Reasoning 50(2), 341–362 (2009)CrossRefzbMATHGoogle Scholar
  19. 19.
    Benito, N., Miro, J.M., Lazzari, E., Cabell, C.H., Rio, A., Altclas, J., Commerford, P., Delahaye, F., Dragulescu, S., Giamarellou, H., Habib, G., Kamarulzaman, A., Sampath, A., Nacinovich, F.M., Suter, F., Tribouilloy, C., Venugopal, K., Moreno, A., Fowler, V.G.: The ICE-PCS (International Collaboration on Endocarditis Prospective Cohort Study) Investigators. Health Care Associated Native Valve Endocarditis: Importance of Non-nosocomial Acquisition. Annals of Internal Medicine 150, 586–594 (2009)CrossRefGoogle Scholar
  20. 20.
    Aamodt, A., Plaza, E.: Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Artificial Intelligence Communications-AICom 7(1), 39–59 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Leticia Curiel
    • 1
  • Bruno Baruque
    • 1
  • Carlos Dueñas
    • 2
  • Emilio Corchado
    • 3
  • Cristina Pérez-Tárrago
    • 2
  1. 1.Department of Civil EngineeringUniversity of BurgosBurgosSpain
  2. 2.Complejo Hospitalario AsistencialUniversitario de Burgos (SACYL), Servicio de Medicina InternaBurgosSpain
  3. 3.Departamento de Informática y AutomáticaUniversidad de SalamancaSalamancaSpain

Personalised recommendations