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Predicting Postoperative Complications for Gastric Cancer Patients Using Data Mining

  • Hugo PeixotoEmail author
  • Alexandra Francisco
  • Ana Duarte
  • Márcia Esteves
  • Sara Oliveira
  • Vítor Lopes
  • António Abelha
  • José Machado
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 273)

Abstract

Gastric cancer refers to the development of malign cells that can grow in any part of the stomach. With the vast amount of data being collected daily in healthcare environments, it is possible to develop new algorithms which can support the decision-making processes in gastric cancer patients treatment. This paper aims to predict, using the CRISP-DM methodology, the outcome from the hospitalization of gastric cancer patients who have undergone surgery, as well as the occurrence of postoperative complications during surgery. The study showed that, on one hand, the RF and NB algorithms are the best in the detection of an outcome of hospitalization, taking into account patients’ clinical data. On the other hand, the algorithms J48, RF, and NB offer better results in predicting postoperative complications.

Keywords

Data Mining Clinical Decision Support Systems CRISP-DM Gastric cancer WEKA 

Notes

Acknowledgments

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

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Hugo Peixoto
    • 1
    Email author
  • Alexandra Francisco
    • 2
  • Ana Duarte
    • 2
  • Márcia Esteves
    • 2
  • Sara Oliveira
    • 2
  • Vítor Lopes
    • 3
  • António Abelha
    • 1
  • José Machado
    • 1
  1. 1.Algoritmi Research CenterUniversity of Minho, Campus GualtarBragaPortugal
  2. 2.University of Minho, Campus GualtarBragaPortugal
  3. 3.Tâmega e Sousa Hospital CenterPenafielPortugal

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