Application of Data Mining for the Prediction of Mortality and Occurrence of Complications for Gastric Cancer Patients
by
Cristiana Neto 1
, Maria Brito 1, Vítor Lopes 2, Hugo Peixoto 1, António Abelha 1 and 
José Machado 1,*
Cristiana Neto 1, Maria Brito 1, Vítor Lopes 2, Hugo Peixoto 1, António Abelha 1 and José Machado 1,*
1
Algoritmi Research Center, University of Minho, 4710-057 Braga, Portugal
2
São João Hospital Center, 4200-319 Porto, Portugal
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(12), 1163; https://doi.org/10.3390/e21121163
Received: 14 November 2019 / Revised: 23 November 2019 / Accepted: 25 November 2019 / Published: 28 November 2019
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
The development of malign cells that can grow in any part of the stomach, known as gastric cancer, is one of the most common causes of death worldwide. In order to increase the survival rate in patients with this condition, it is essential to improve the decision-making process leading to a better and more efficient selection of treatment strategies. Nowadays, with the large amount of information present in hospital institutions, it is possible to use data mining algorithms to improve the healthcare delivery. Thus, this study, using the CRISP methodology, aims to predict not only the mortality associated with this disease, but also the occurrence of any complication following surgery. A set of classification models were tested and compared in order to improve the prediction accuracy. The study showed that, on one hand, the J48 algorithm using oversampling is the best technique to predict the mortality in gastric cancer patients, with an accuracy of approximately 74%. On the other hand, the rain forest algorithm using oversampling presents the best results when predicting the possible occurrence of complications among gastric cancer patients after their in-hospital stays, with an accuracy of approximately 83%.
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Keywords:
healthcare; gastric cancer; knowledge discovery in databases; data mining; classification; prediction; clinical decision support systems; CRISP-DM; WEKA
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MDPI and ACS Style
Neto, C.; Brito, M.; Lopes, V.; Peixoto, H.; Abelha, A.; Machado, J. Application of Data Mining for the Prediction of Mortality and Occurrence of Complications for Gastric Cancer Patients. Entropy 2019, 21, 1163.
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