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BY-NC-ND 4.0 license Open Access Published by De Gruyter Open Access December 29, 2017

Machine Learning in Nutritional Follow-up Research

Rita Reis, Hugo Peixoto, José Machado and António Abelha
From the journal Open Computer Science


Healthcare is one of the world’s fastest growing industries, having large volumes of data collected on a daily basis. It is generally perceived as being ‘information rich’ yet ‘knowledge poor’. Hidden relationships and valuable knowledge can be discovered in the collected data from the application of data mining techniques. These techniques are being increasingly implemented in healthcare organizations in order to respond to the needs of doctors in their daily decision-making activities. To help the decision-makers to take the best decision it is fundamental to develop a solution able to predict events before their occurrence. The aim of this project was to predict if a patient would need to be followed by a nutrition specialist, by combining a nutritional dataset with data mining classification techniques, using WEKA machine learning tools. The achieved results showed to be very promising, presenting accuracy around 91%, specificity around 97% and precision about 95%.


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Received: 2017-11-17
Accepted: 2017-11-29
Published Online: 2017-12-29
Published in Print: 2017-12-20

© 2018

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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