2017
Reis, R.; Peixoto, H.; Machado, J.; Abelha, A.
Machine Learning in Nutritional Follow-up Research Journal Article
In: Open Computer Science, vol. 7, no. 1, pp. 41-45, 2017, ISSN: 22991093, (cited By 15).
Abstract | Links | BibTeX | Etiquetas: Best decision; Decision makers; Follow up; Healthcare organizations; Knowledge-poor; Large volumes; Mining classification, Classification (of information); Decision making; Large dataset; Machine learning; Nutrition, Data mining
@article{Reis201741,
title = {Machine Learning in Nutritional Follow-up Research},
author = {R. Reis and H. Peixoto and J. Machado and A. Abelha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052023664&doi=10.1515%2fcomp-2017-0008&partnerID=40&md5=6535e510ffa66e106037cdc4c24125a5},
doi = {10.1515/comp-2017-0008},
issn = {22991093},
year = {2017},
date = {2017-01-01},
journal = {Open Computer Science},
volume = {7},
number = {1},
pages = {41-45},
publisher = {Walter de Gruyter GmbH},
abstract = {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%. © 2017 R. Reis et al.},
note = {cited By 15},
keywords = {Best decision; Decision makers; Follow up; Healthcare organizations; Knowledge-poor; Large volumes; Mining classification, Classification (of information); Decision making; Large dataset; Machine learning; Nutrition, Data mining},
pubstate = {published},
tppubtype = {article}
}
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%. © 2017 R. Reis et al.