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Open Computer Science

Editor-in-Chief: van den Broek, Egon


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CiteScore 2018: 0.63
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2299-1093
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A Data Mining Approach for Cardiovascular Diagnosis

Joana Pereira / Hugo Peixoto / José Machado / António Abelha
Published Online: 2017-12-29 | DOI: https://doi.org/10.1515/comp-2017-0007

Abstract

The large amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analysed by traditional methods. Data mining can improve decision-making by discovering patterns and trends in large amounts of complex data. In the healthcare industry specifically, data mining can be used to decrease costs by increasing efficiency, improve patient quality of life, and perhaps most importantly, save the lives of more patients. The main goal of this project is to apply data mining techniques in order to make possible the prediction of the degree of disability that patients will present when they leave hospitalization. The clinical data that will compose the data set was obtained from one single hospital and contains information about patients who were hospitalized in Cardio Vascular Disease’s (CVD) unit in 2016 for having suffered a cardiovascular accident. To develop this project, it will be used the Waikato Environment for Knowledge Analysis (WEKA) machine learning Workbench since this one allows users to quickly try out and compare different machine learning methods on new data sets

Keywords: healthcare information systems; knowledge discovery; data mining; machine learning; classification algorithms; cerebrovascular accidents

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About the article

Received: 2017-11-17

Accepted: 2017-11-29

Published Online: 2017-12-29

Published in Print: 2017-12-20


Citation Information: Open Computer Science, Volume 7, Issue 1, Pages 36–40, ISSN (Online) 2299-1093, DOI: https://doi.org/10.1515/comp-2017-0007.

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© 2017 Joana Pereira et al. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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