loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Nikos Fazakis ; Elias Dritsas ; Otilia Kocsis ; Nikos Fakotakis and Konstantinos Moustakas

Affiliation: Department of Electrical and Computer Engineering, University of Patras, 26504 Rion, Greece

Keyword(s): Cholesterol, Long-term Prediction, Machine Learning.

Abstract: Cholesterol is a crucial risk factor for cardiovascular diseases (CVDs) which in their turn are among the main causes of death worldwide and public health concern, with heart diseases being the most prevalent ones. For cholesterol control, the early prediction is considered one of the most effective ways. Utilizing the English Longitudinal Study of Ageing (ELSA), a large-scale database of ageing participants, a dataset is derived to evaluate the long-term cholesterol risk of elderly men and women using Machine Learning (ML) techniques. Several ML prediction models were assessed concerning Accuracy and Recall where the Logistic model tree was the best performer. The ultimate goal of this study is to identify individuals at risk and facilitate earlier intervention to prevent the future development of cholesterol.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.217.194.39

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Fazakis, N.; Dritsas, E.; Kocsis, O.; Fakotakis, N. and Moustakas, K. (2021). Long-term Cholesterol Risk Prediction using Machine Learning Techniques in ELSA Database. In Proceedings of the 13th International Joint Conference on Computational Intelligence - SmartWork; ISBN 978-989-758-534-0; ISSN 2184-3236, SciTePress, pages 445-450. DOI: 10.5220/0010727200003063

@conference{smartwork21,
author={Nikos Fazakis. and Elias Dritsas. and Otilia Kocsis. and Nikos Fakotakis. and Konstantinos Moustakas.},
title={Long-term Cholesterol Risk Prediction using Machine Learning Techniques in ELSA Database},
booktitle={Proceedings of the 13th International Joint Conference on Computational Intelligence - SmartWork},
year={2021},
pages={445-450},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010727200003063},
isbn={978-989-758-534-0},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computational Intelligence - SmartWork
TI - Long-term Cholesterol Risk Prediction using Machine Learning Techniques in ELSA Database
SN - 978-989-758-534-0
IS - 2184-3236
AU - Fazakis, N.
AU - Dritsas, E.
AU - Kocsis, O.
AU - Fakotakis, N.
AU - Moustakas, K.
PY - 2021
SP - 445
EP - 450
DO - 10.5220/0010727200003063
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic