Application of Data Mining for the Prediction of Mortality and Occurrence of Complications for Gastric Cancer Patients

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 a...

Full description

Saved in:
Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2019-11, Vol.21 (12), p.1163
Main Authors: Neto, Cristiana, Brito, Maria, Lopes, Vítor, Peixoto, Hugo, Abelha, António, Machado, José
Format: Article
Language:English
Subjects:
Algorithms
Cancer
Data mining
Decision making
Decision support systems
Gastric cancer
Knowledge discovery
Machine learning
Mathematical functions
Medical research
Mortality
Oversampling
Patients
Rainforests
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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%.
ISSN:1099-4300
1099-4300
DOI:10.3390/e21121163