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Authors: Alice Pintanel 1 ; Graçaliz Dimuro 2 ; Eduardo Nunes Borges 2 ; Giancarlo Lucca 2 and Camila Barcelos 3

Affiliations: 1 Computational Modeling, Federal University of Rio Grande (FURG), Rio Grande, RS, Brazil ; 2 Center for Computational Sciences (C3), Federal University of Rio Grande (FURG), Rio Grande, RS, Brazil ; 3 Hospital Sirio Libanes, São Paulo, SP, Brazil

Keyword(s): Diabetes, Machine Learning, Classification Problems, Systematic Literature Review.

Abstract: The use of methodologies based on machine learning is being increasingly used in health systems today, addressing different areas such as food, society, health and others. In terms of health, different techniques were applied to classify different diseases. In this sense, diabetes is an important and silent disease that deserves special attention and care. Individuals often do not know they have it, and, therefore, seeking alternatives to predict this disease is an important contribution to the health area. Thinking about it, in this work we present a systematic review of the literature with the objective of observing which strategies are currently being used to predict and classify diseases using fuzzy logic, in particular, diabetes. For this, 6 works were selected and analyzed, where the technique for obtaining the considered information is the blood test, in order to understand the current state of the art.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Pintanel, A.; Dimuro, G.; Nunes Borges, E.; Lucca, G. and Barcelos, C. (2023). Fuzzy Logic for Diabetes Predictions: A Literature Review. In Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-648-4; ISSN 2184-4992, SciTePress, pages 476-483. DOI: 10.5220/0011851500003467

@conference{iceis23,
author={Alice Pintanel. and Gra\c{C}aliz Dimuro. and Eduardo {Nunes Borges}. and Giancarlo Lucca. and Camila Barcelos.},
title={Fuzzy Logic for Diabetes Predictions: A Literature Review},
booktitle={Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2023},
pages={476-483},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011851500003467},
isbn={978-989-758-648-4},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Fuzzy Logic for Diabetes Predictions: A Literature Review
SN - 978-989-758-648-4
IS - 2184-4992
AU - Pintanel, A.
AU - Dimuro, G.
AU - Nunes Borges, E.
AU - Lucca, G.
AU - Barcelos, C.
PY - 2023
SP - 476
EP - 483
DO - 10.5220/0011851500003467
PB - SciTePress