2018
Ferreira, D.; Peixoto, H.; Machado, J.; Abelha, A.
Predictive data mining in nutrition therapy Proceedings Article
Em: C., Gil P. Henriques J. Teixeira (Ed.): pp. 137-142, Institute of Electrical and Electronics Engineers Inc., 2018, ISBN: 9781538653463, (cited By 9; Conference of 13th APCA International Conference on Control and Soft Computing, CONTROLO 2018 ; Conference Date: 4 June 2018 Through 6 June 2018; Conference Code:141782).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Classification (of information); Health care; Information technology; Learning systems; Linear transformations; Medical computing; Metadata; Nutrition; Soft computing, Classification algorithm; Clinical decision; Confusion matrices; Data transformation; Healthcare industry; Knowledge analysis; Performance measure; Predictive data mining, Data mining
@inproceedings{Ferreira2018137,
title = {Predictive data mining in nutrition therapy},
author = {D. Ferreira and H. Peixoto and J. Machado and A. Abelha},
editor = {Gil P. Henriques J. Teixeira C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057334712&doi=10.1109%2fCONTROLO.2018.8516413&partnerID=40&md5=dee7234bc455a021e666a52be4dbb1fe},
doi = {10.1109/CONTROLO.2018.8516413},
isbn = {9781538653463},
year = {2018},
date = {2018-01-01},
journal = {13th APCA International Conference on Control and Soft Computing, CONTROLO 2018 - Proceedings},
pages = {137-142},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The assessment and measurement of health status in communities throughput the world is a massive information technology challenge. Data mining, plays a vital role in health care industry since it really has the potential to generate a knowledge-rich environment that reduces medical errors, decreases costs by increasing efficiency, improves the quality of clinical decisions and significantly enhances patient's outcomes and quality of life. This study falls within the context of nutrition evaluation and its main goal is to apply classification algorithms in order to predict if a patient needs to be followed by a nutrition specialist. One of the tools resorted in this study was the Waikato Environment for Knowledge Analysis (Weka in advance) Workbench since it allows to quickly try out and compare different machine learning solutions. The tasks involved in the development of this project included data preparation, data preprocessing, data transformation and cleaning, application of several classifiers and its respective evaluation through performance measures that include the confusion matrix, accuracy, error rate, and others. The accomplished results showed to be quite optimistic presenting promising values of performance measures. specifically an accuracy around 91 %. © 2018 IEEE.},
note = {cited By 9; Conference of 13th APCA International Conference on Control and Soft Computing, CONTROLO 2018 ; Conference Date: 4 June 2018 Through 6 June 2018; Conference Code:141782},
keywords = {Artificial intelligence; Classification (of information); Health care; Information technology; Learning systems; Linear transformations; Medical computing; Metadata; Nutrition; Soft computing, Classification algorithm; Clinical decision; Confusion matrices; Data transformation; Healthcare industry; Knowledge analysis; Performance measure; Predictive data mining, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
The assessment and measurement of health status in communities throughput the world is a massive information technology challenge. Data mining, plays a vital role in health care industry since it really has the potential to generate a knowledge-rich environment that reduces medical errors, decreases costs by increasing efficiency, improves the quality of clinical decisions and significantly enhances patient's outcomes and quality of life. This study falls within the context of nutrition evaluation and its main goal is to apply classification algorithms in order to predict if a patient needs to be followed by a nutrition specialist. One of the tools resorted in this study was the Waikato Environment for Knowledge Analysis (Weka in advance) Workbench since it allows to quickly try out and compare different machine learning solutions. The tasks involved in the development of this project included data preparation, data preprocessing, data transformation and cleaning, application of several classifiers and its respective evaluation through performance measures that include the confusion matrix, accuracy, error rate, and others. The accomplished results showed to be quite optimistic presenting promising values of performance measures. specifically an accuracy around 91 %. © 2018 IEEE.