Abstract
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%.
Highlights
Many aspects that were previously unknown to healthcare professionals are being revealed by the data generated by healthcare, improving the quality of medical procedures or treatment strategies [1]
We found that the post-operative recovery is an important predictor of the mortality of this cancer, since patients which demand ICU
The useful information discovered and the patterns obtained with the application of these methods, analysing in real-time complex and heterogeneous data and make conclusions about it, can be used by health professionals to determine diagnoses, prognoses and treatments for patients in healthcare organizations
Summary
Many aspects that were previously unknown to healthcare professionals are being revealed by the data generated by healthcare, improving the quality of medical procedures or treatment strategies [1]. Healthcare facilities like hospitals produce large amounts of heterogeneous data every day, since it includes diverse sources, data types and formats. This heterogeneity of healthcare data leads to the need of a rigorous observation of this data in order to assess its quality and identify possible problems that need to be solved. Since the data are so complex, it is practically impossible to analyze it with traditional tools and methods [2]. This complexity calls for more sophisticated techniques that are able to manage and produce meaningful knowledge. The use of data technologies like data mining (DM) has become essential in healthcare
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