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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.

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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 2184-2825, 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={2184-2825},
}

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 - 2184-2825
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