Knowledge-Based System for Diagnosis of Metabolic Alterations in Undergraduate Students

  • Miguel Murguía-Romero
  • René Méndez-Cruz
  • Rafael Villalobos-Molina
  • Norma Yolanda Rodríguez-Soriano
  • Estrella González-Dalhaus
  • Rafael Jiménez-Flores
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6437)


A knowledge based system to identify 10 main metabolic alterations in university students based on clinical and anthropometric parameters is presented. Knowledge engineering was carried out through unstructured expert interviews methodology, resulting in a knowledge base of 17 IF-THEN rules. A backward chaining machine engine was built in Prolog language; the attribute-values database about parameters of each student was also stored in Prolog facts. The system was applied to 592 cases: clinical and anthropometric parameters of the students stored in the database. Medical diagnoses and recommendations for each student, obtained from the system, were organized in individualized reports that the physicians gave to the students in personal interviews along only two days. The effectiveness of these interviews is largely attributed to the fact that physicians are the same experts who participated in the process of building the knowledge base.


Knowledge-based systems medical diagnosis metabolic syndrome Prolog 


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  1. 1.
    de Salud, S.: Acuerdo nacional para la salud alimentaria. Secretaría de Salud, México (2010),
  2. 2.
    Córdova-Villalobos, J.A.: Implementation in Mexico of the national agreement for nutrition and health as a strategy against overweight and obesity. Cir. Cir. 78, 105–107 (2010)Google Scholar
  3. 3.
    Misra, A., Khurana, L.: Obesity and the metabolic syndrome in developing countries. J. Clin. Endocrinol. Metab. 93, pp. S9–S30 (2008)Google Scholar
  4. 4.
    Rio-Navarro, B., Velzquez-Monroy, O., Snchez-Castillo, C.: The high prevalence of overweight and obesity in mexican children. Obes. Res. 12, 215–223 (2004)CrossRefGoogle Scholar
  5. 5.
    Grundy, S., Cleeman, J., Daniels, S.: Diagnosis and management of the metabolic syndrome. an american heart association/national heart, lung, and blood institute scientific statement. Circulation 112, 2735–2752 (2005)CrossRefGoogle Scholar
  6. 6.
    Shortliffe, E.H., Axline, S.G., Buchanan, B.G., Merigan, T.C., Cohen, S.N.: An artificial intelligence program to advice physicians regarding antimicrobial therapy. Comput. Biomed. Res. 6, 544–560 (1973)CrossRefGoogle Scholar
  7. 7.
    Waterman, D.: A guide to Expert Systems. Addison-Wesley, M.A. (1986)Google Scholar
  8. 8.
    Bottino, D.: Decision support system on hypertension treatment. In: World Congress on Medical Physics and Biomedical Engineering, Germany, pp. 474–476. Springer, Heidelberg (2009)Google Scholar
  9. 9.
    Thornet, A.M.: Computer decision support systems in general practice. International Journal of Information Management 21, 39–47 (2001)CrossRefGoogle Scholar
  10. 10.
    Biermann, E., Rihl, J., Schenker, M., Standl, E.: Semi-automatic generation of medical tele-expert opinion for primary care physician. Methods Inf. Med. 43, 212–219 (2003)Google Scholar
  11. 11.
    Liao, S.: Expert system methodologies and applications-a decade review from 1995 to 2004. Expert Systems with Applications 28, 93–103 (2005)CrossRefGoogle Scholar
  12. 12.
    Coffey, J.W., Hoffman, R.R.: Knowledge modeling for the preservation of institutional memory. Journal of Knowledge Management 7, 38–52 (2003)CrossRefGoogle Scholar
  13. 13.
    Akerkar, R., Sajia, P.: Knowledge-Based Systems. Jones and Bartlett Publishers, USA (2010)Google Scholar
  14. 14.
    Hoffman, R.R., Shadbolt, N.R., Burton, A.M., Klein, G.: Eliciting knowledge from experts: A methodological analysis. Behavior and Human Decision Processes 62, 129–158 (1995)CrossRefGoogle Scholar
  15. 15.
    Shortliffe, E.H., Davis, R., Axline, S.G., Buchanan, B.G., Green, C.C., Cohen, S.N.: Computer-based consultations in clinical therapeutics: explanation and rule-acquisition capabilities of the mycin system. Comput. Biomed. Res. 8, 303–320 (1975)CrossRefGoogle Scholar
  16. 16.
    Lenz, R., Reichert, M.: It support for healthcare processes - premises, challenges, perspectives. Data and Knowledge Engineering 61, 39–58 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Miguel Murguía-Romero
    • 1
  • René Méndez-Cruz
    • 2
  • Rafael Villalobos-Molina
    • 1
  • Norma Yolanda Rodríguez-Soriano
    • 3
  • Estrella González-Dalhaus
    • 4
  • Rafael Jiménez-Flores
    • 2
  1. 1.Unidad de Investigación en BiomedicinaMéxico
  2. 2.Carrera de Médico CirujanoUniversidad Nacional Autónoma de MéxicoLos Reyes IztacalaMéxico
  3. 3.Carrera de Psicología , Facultad de Estudios Superiores IztacalaUniversidad Nacional Autónoma de MéxicoLos Reyes IztacalaMéxico
  4. 4.Universidad Autónoma de la Ciudad de MéxicoSan Lorenzo Tezonco, IztapalapaMéxico

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