Predicting Death and Morbidity in Perforated Peptic Ulcer

  • Hugo Peixoto
  • Lara Correia e Silva
  • Soraia Pereira
  • Tiago Jesus
  • Vítor Lopes
  • António Abelha
  • José MachadoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 918)


Peptic ulcers are defined as defects in the gastrointestinal mucosa that extend through the muscularis mucosae. Although not being the most common complication, perforations stand out as being the complication with the highest mortality rate. To predict the probability of mortality, several scoring systems based on clinical and biochemical parameters, such as the Boey and PULP scoring system have been developed. This article explores, using data mining in the medical data available, how the scoring systems perform when trying to predict mortality and patients’ state complication. We also try to conclude, from the two scoring systems presented, which predicts better the situations described. Regarding the results, we concluded that the PULP scoring allows a better mortality prediction achieving, in this case, above 90% accuracy, however, the results may be inconclusive due to the lack of patients who died in the dataset used. Regarding the complications, we concluded that, on the other hand, the Boey system achieves better results leading to a better prediction when it comes to predicting patients’ state complication.


Data mining Dataset Scoring systems PULP Boey Death Health complications 



This work has been supported by Compete: POCI-01-0145-FEDER-007043 and FCT within the Project Scope UID/CEC/00319/2013.


  1. 1.
    Sandler, R.S., Everhart, J.E., Donowitz, M., et al.: The burden of selected digestive diseases in the United States. Gastroenterology 122(5), 1500–1511 (2002)CrossRefGoogle Scholar
  2. 2.
    Wang, Y.R., Richter, J.E., Dempsey, D.T.: Trends and outcomes of hospitalizations for peptic ulcer disease in the United States, 1993 to 2006. Ann. Surg. 251(1), 51 (2010)CrossRefGoogle Scholar
  3. 3.
    Irabor, D.O.: An audit of peptic ulcer surgery in Ibadan, Nigeria. West Afr. J. Med. 24(3), 242 (2005)Google Scholar
  4. 4.
    Sánchez-Delgado, J., Gené, E., Suárez, D., García-Iglesias, P., et al.: Has H. pylori prevalence in bleeding peptic ulcer been underestimated? A meta-regression. Am. J. Gastroenterol. 106(3), 398 (2011)CrossRefGoogle Scholar
  5. 5.
    Lohsiriwat, V., Prapasrivorakul, S., Lohsiriwat, D.: Perforated peptic ulcer: clinical presentation, surgical outcomes, and the accuracy of the Boey scoring system in predicting postoperative morbidity and mortality. World J. Surg. 33, 80–85 (2009)CrossRefGoogle Scholar
  6. 6.
    Møller, M.H., Engebjerg, M.C., Adamsen, S., Bendix, J., Thomsen, R.W.: The Peptic Ulcer Perforation (PULP) score: a predictor of mortality following peptic ulcer perforation. A cohort study. Acta Anaesthesiol. Scand. 56(5), 655–662 (2012)CrossRefGoogle Scholar
  7. 7.
    Neto, C., Peixoto, H., Abelha, V., Abelha, A., Machado, J.: Knowledge discovery from surgical waiting lists. Procedia Comput. Sci. 121, 1104–1111 (2017)CrossRefGoogle Scholar
  8. 8.
    Zhao, Y.: R and Data Mining: Examples and Case Studies. Academic Press, San Diego (2012)Google Scholar
  9. 9.
    Pereira, J., Peixoto, H., Machado, J., Abelha, A.: A data mining approach for cardiovascular diagnosis. Open Comput. Sci. 7(1), 36–40 (2017)CrossRefGoogle Scholar
  10. 10.
    Cohen, W.W.: Fast effective rule induction. In: Machine Learning Proceedings 1995, pp. 115–123 (1995)Google Scholar
  11. 11.
    Kohavi, R.: The power of decision tables. In: European Conference on Machine Learning, pp. 174–189. Springer, Heidelberg, April 1995Google Scholar
  12. 12.
    Najm, W.I.: Peptic ulcer disease. Prim. Care Clin. Off. Pract. 38(3), 383–394 (2011)CrossRefGoogle Scholar
  13. 13.
    Parsania, V., Bhalodiya, N., Jani, N.N.: Applying Naïve Bayes, BayesNet, PART, JRip and OneR Algorithms on Hypothyroid Database for Comparative Analysis (2014)Google Scholar
  14. 14.
    Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., Zhou, Z.H., Steinbach, M., Hand, D.J., Steinberg, D.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1–37 (2008)CrossRefGoogle Scholar
  15. 15.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Elsevier, Amsterdam (2014)Google Scholar
  16. 16.
    Arthur, D., Vassilvitskii, S.: k-means ++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. Society for Industrial and Applied Mathematics, January 2007Google Scholar
  17. 17.
    Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.: CRISP-DM 1.0 Step-by-step data mining guide (2000)Google Scholar
  18. 18.
    Reis, R., Peixoto, H., Machado, J., Abelha, A.: Machine learning in nutritional follow-up research. Open Comput. Sci. 7(1), 41–45 (2017)CrossRefGoogle Scholar
  19. 19.
    Morais, A., Peixoto, H., Coimbra, C., Abelha, A., Machado, J.: Predicting the need of Neonatal Resuscitation using Data Mining. Procedia Comput. Sci. 113, 571–576 (2017)CrossRefGoogle Scholar
  20. 20.
    Hand, D.J.: Principles of data mining. Drug Saf. 30(7), 621–622 (2007)CrossRefGoogle Scholar
  21. 21.
    Cios, K.J., Moore, G.W.: Uniqueness of medical data mining. Artif. Intell. Med. 26(1–2), 1–24 (2002)CrossRefGoogle Scholar
  22. 22.
    Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K., Gopidi, R., Chang, J.F., Hua, L.: Data mining in healthcare and biomedicine: a survey of the literature. J. Med. Syst. 36(4), 2431–2448 (2012)CrossRefGoogle Scholar
  23. 23.
    Thorsen, K., Søreide, J.A., Søreide, K.: Scoring systems for outcome prediction in patients with perforated peptic ulcer. Scand. J. Trauma Resusc. Emerg. Med. 21(1), 25 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hugo Peixoto
    • 1
  • Lara Correia e Silva
    • 2
  • Soraia Pereira
    • 2
  • Tiago Jesus
    • 2
  • Vítor Lopes
    • 3
  • António Abelha
    • 1
  • José Machado
    • 1
    Email author
  1. 1.Algoritmi Research CenterUniversity of MinhoBragaPortugal
  2. 2.University of MinhoBragaPortugal
  3. 3.Centro Hospitalar do Tâmega e SousaPenafielPortugal

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