Machine learning approaches for diabetes classification: Perspectives to artificial intelligence methods updating
Published in IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security, 2020
Recommended citation: Giuseppe Mainenti, Lelio Campanile, Fiammetta Marulli, Carlo Ricciardi, Antonio Valente, "Machine learning approaches for diabetes classification: Perspectives to artificial intelligence methods updating." IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security, 2020. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089519717&partnerID=40&md5=bf7cc36e86c1988dd85e04c2fce06de1
Cited by: 3
Abstract: In recent years the application of Machine Learning (ML) and Artificial Intelligence (AI) techniques in healthcare helped clinicians to improve the management of chronic patients. Diabetes is among the most common chronic illness in the world for which often is still challenging do an early detection and a correct classification of type of diabetes to an individual. In fact it often depends on the circumstances present at the time of diagnosis, and many diabetic individuals do not easily fit into a single class. The aim is this paper is the application of ML techniques in order to classify the occurrence of different mellitus diabetes on the base of clinical data obtained from diabetic patients during the daily hospitals activities. Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
Author Keywords: Artificial Intelligence; Big Data Analytics; Diabetes Classification; Diabetes Management; Machine Learning
Bibtex citation:
@CONFERENCE{Mainenti2020533,
author = "Mainenti, Giuseppe and Campanile, Lelio and Marulli, Fiammetta and Ricciardi, Carlo and Valente, Antonio S.",
title = "Machine learning approaches for diabetes classification: Perspectives to artificial intelligence methods updating",
year = "2020",
journal = "IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security",
pages = "533 – 540",
type = "Conference paper"
}