Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
Irabor, D.O.: An audit of peptic ulcer surgery in Ibadan, Nigeria. West Afr. J. Med. 24(3), 242 (2005)
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)
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)
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)
Neto, C., Peixoto, H., Abelha, V., Abelha, A., Machado, J.: Knowledge discovery from surgical waiting lists. Procedia Comput. Sci. 121, 1104–1111 (2017)
Zhao, Y.: R and Data Mining: Examples and Case Studies. Academic Press, San Diego (2012)
Pereira, J., Peixoto, H., Machado, J., Abelha, A.: A data mining approach for cardiovascular diagnosis. Open Comput. Sci. 7(1), 36–40 (2017)
Cohen, W.W.: Fast effective rule induction. In: Machine Learning Proceedings 1995, pp. 115–123 (1995)
Kohavi, R.: The power of decision tables. In: European Conference on Machine Learning, pp. 174–189. Springer, Heidelberg, April 1995
Najm, W.I.: Peptic ulcer disease. Prim. Care Clin. Off. Pract. 38(3), 383–394 (2011)
Parsania, V., Bhalodiya, N., Jani, N.N.: Applying Naïve Bayes, BayesNet, PART, JRip and OneR Algorithms on Hypothyroid Database for Comparative Analysis (2014)
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)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Elsevier, Amsterdam (2014)
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 2007
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)
Reis, R., Peixoto, H., Machado, J., Abelha, A.: Machine learning in nutritional follow-up research. Open Comput. Sci. 7(1), 41–45 (2017)
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)
Hand, D.J.: Principles of data mining. Drug Saf. 30(7), 621–622 (2007)
Cios, K.J., Moore, G.W.: Uniqueness of medical data mining. Artif. Intell. Med. 26(1–2), 1–24 (2002)
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)
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)
Acknowledgments
This work has been supported by Compete: POCI-01-0145-FEDER-007043 and FCT within the Project Scope UID/CEC/00319/2013.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Peixoto, H. et al. (2019). Predicting Death and Morbidity in Perforated Peptic Ulcer. In: Rocha, Á., Ferrás, C., Paredes, M. (eds) Information Technology and Systems. ICITS 2019. Advances in Intelligent Systems and Computing, vol 918. Springer, Cham. https://doi.org/10.1007/978-3-030-11890-7_54
Download citation
DOI: https://doi.org/10.1007/978-3-030-11890-7_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11889-1
Online ISBN: 978-3-030-11890-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)