Predicting the Length of Hospital Stay After Surgery for Perforated Peptic Ulcer

  • José MachadoEmail author
  • Ana Catarina Cardoso
  • Inês Gomes
  • Inês Silva
  • Vítor Lopes
  • Hugo Peixoto
  • António Abelha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 918)


The management of peptic ulcer disease usually implies an urgent surgical procedure with the need of a patient’s hospital admission. By predicting the length of hospital stay of patients, improvements can be made regarding the quality of services provided to patients. This paper focuses on using real data to identify patterns in patients’ profiles and surgical events, in order to predict if patients will need hospital care for a shorter or longer period of time. This goal is pursued using a Data Mining process which follows the CRISP-DM methodology. In particular, classification models are built by combining different scenarios, algorithms and sampling methods. The data mining model which performed best achieved an accuracy of 87.30%, a specificity of 89.40%, and a sensitivity of 81.30%, using JRip, a rule-based algorithm and Cross Validation as a sampling method.


Perforated peptic ulcer Length of hospital stay Data mining CRISP-DM Classification Decision Support Systems 



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


  1. 1.
    Lin, K.J., García Rodríguez, L.A., Díaz, S.H.: Systematic review of peptic ulcer disease incidence rates: do studies without validation provide reliable estimates? Pharmacoepidemiol. Drug Saf. 20, 718–728 (2011)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, 51–58 (2010)CrossRefGoogle Scholar
  3. 3.
    Irabor, D.O.: An audit of peptic ulcer surgery in Ibadan. Nigeria. West Afr. J. Med. 24, 242–245 (2005)Google Scholar
  4. 4.
    Li, C.H., Bair, M.J., Chang, W.H., Shih, S.C., Lin, S.C., Yeh, C.Y.: Predictive model for length of hospital stay of patients surviving surgery for perforated peptic ulcer. J. Formos. Med. Assoc. 108, 644–652 (2009)CrossRefGoogle Scholar
  5. 5.
    Caetano, N., Cortez, P., Laureano, R.M.S.: Using data mining for prediction of hospital length of stay: an application of the CRISP-DM methodology. In: Cordeiro, J., Hammoudi, S., Maciaszek, L., Camp, O., Filipe, J. (eds.) International Conference on Enterprise Information Systems, pp. 149–166. Springer, Cham (2015)CrossRefGoogle Scholar
  6. 6.
    Pereira, J.J.R.: Modelos de data mining para multi-previsão: Aplicação à medicina intensiva (2005)Google Scholar
  7. 7.
    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
  8. 8.
    Pereira, J., Peixoto, H., Machado, J., Abelha, A.: A data mining approach for cardiovascular diagnosis. Open Comput. Sci. 7, 36–40 (2017)CrossRefGoogle Scholar
  9. 9.
    Reis, R., Peixoto, H., Machado, J., Abelha, A.: Machine learning in nutritional follow-up research. Open Comput. Sci. 7, 41–45 (2017)CrossRefGoogle Scholar
  10. 10.
    Milovic, B., Milovic, M.: Prediction and decision making in health care using data mining. Int. J. Public Health Sci. 1, 69–78 (2012)Google Scholar
  11. 11.
    Malfertheiner, P., Chan, F.K.L., McColl, K.E.L.: Peptic ulcer disease. Lancet 374, 1449–1461 (2009)CrossRefGoogle Scholar
  12. 12.
    Sanabria, A., Villegas, M.I., Uribe, C.H.M.: Laparoscopic repair for perforated peptic ulcer disease. Cochrane Database Syst. Rev. (2013)Google Scholar
  13. 13.
    Bhogal, R.H., Athwal, R., Durkin, D., Deakin, M., Cheruvu, C.N.V.: Comparison between open and laparoscopic repair of perforated peptic ulcer disease. World J. Surg. 32, 2371–2374 (2008)CrossRefGoogle Scholar
  14. 14.
    Bertleff, M.J.O.E., Lange, J.F.: Laparoscopic correction of perforated peptic ulcer: first choice? A review of literature. Surg. Endosc. 24, 1231–1239 (2010)CrossRefGoogle Scholar
  15. 15.
    American Society of Anesthesiologists: ASA physical status classification system (2014)Google Scholar
  16. 16.
    Deshmukh, B., Patil, A.S., Pawar, B.V.: Comparison of classification algorithms using WEKA on various datasets. Int. J. Comput. Sci. Inf. Technol. 4, 85–90 (2011)Google Scholar
  17. 17.
    Rodrigues, M., Peixoto, H., Esteves, M., Machado, J., Abelha, A.: Understanding stroke in dialysis and chronic kidney disease. Procedia Comput. Sci. 113, 591–596 (2017)CrossRefGoogle Scholar
  18. 18.
    Landwehr, N., Hall, M., Frank, E.: Logistic model trees. Mach. Learn. 59, 161–205 (2005)CrossRefGoogle Scholar
  19. 19.
    Platt, J.: Sequential minimal optimization: a fast algorithm for training support vector machines (1998)Google Scholar
  20. 20.
    Bhargava, N., Jain, A., Kumar, A., Dac-Nhuong, L.: Detection of malicious executables using rule based classification algorithms. In: Jaiswal, A., Solanki, V.K., Lu, Z. (Joan), Rajput, N. (eds.) Proceedings of the First International Conference on Information Technology and Knowledge Management, pp. 35–38. PTI (2018)Google Scholar
  21. 21.
    Solanki, A.V.: Data mining techniques using WEKA classification for sickle cell disease. Int. J. Comput. Sci. Inf. Technol. 5, 5857–5860 (2014)Google Scholar
  22. 22.
    Stojanova, D., Panov, P., Kobler, A., Džeroski, S., Taškova, K.: Learning to predict forest fires with different data mining techniques. In: Conference on Data Mining and Data Warehouses, pp. 255–258 (2006)Google Scholar
  23. 23.
    IBM: IBM SPSS Modeler CRISP-DM Guide (2011)Google Scholar
  24. 24.
    Doreswamy, H.K.S.: Performance evaluation of predictive classifiers for knowledge discovery from engineering materials data sets. CIIT Int. J. Artif. Intell. Syst. Mach. Learn. 3, 162–168 (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • José Machado
    • 1
    Email author
  • Ana Catarina Cardoso
    • 2
  • Inês Gomes
    • 2
  • Inês Silva
    • 2
  • Vítor Lopes
    • 3
  • Hugo Peixoto
    • 1
  • António Abelha
    • 1
  1. 1.Algoritmi Research CenterUniversity of MinhoBragaPortugal
  2. 2.University of MinhoBragaPortugal
  3. 3.Centro Hospitalar do Tâmega e SousaPenafielPortugal

Personalised recommendations