Research Article
Predicting Postoperative Complications for Gastric Cancer Patients Using Data Mining
@INPROCEEDINGS{10.1007/978-3-030-16447-8_4, author={Hugo Peixoto and Alexandra Francisco and Ana Duarte and M\^{a}rcia Esteves and Sara Oliveira and V\^{\i}tor Lopes and Ant\^{o}nio Abelha and Jos\^{e} Machado}, title={Predicting Postoperative Complications for Gastric Cancer Patients Using Data Mining}, proceedings={Intelligent Technologies for Interactive Entertainment. 10th EAI International Conference, INTETAIN 2018, Guimar\"{a}es, Portugal, November 21-23, 2018, Proceedings}, proceedings_a={INTETAIN}, year={2019}, month={4}, keywords={Data Mining Clinical Decision Support Systems CRISP-DM Gastric cancer WEKA}, doi={10.1007/978-3-030-16447-8_4} }- Hugo Peixoto
Alexandra Francisco
Ana Duarte
Márcia Esteves
Sara Oliveira
Vítor Lopes
António Abelha
José Machado
Year: 2019
Predicting Postoperative Complications for Gastric Cancer Patients Using Data Mining
INTETAIN
Springer
DOI: 10.1007/978-3-030-16447-8_4
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.
