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Authors: Elias Dritsas ; Sotiris Alexiou and Konstantinos Moustakas

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

Keyword(s): CVDs, Machine Learning, Risk Prediction.

Abstract: Cardiovascular diseases (CVDs) are the leading cause of death worldwide and a major public health concern, with heart diseases being the most prevalent ones, thus the early prediction is being considered as one of the most effective measures for CVDs control. The risk evaluation for CVD occurrence on participants (men and women) especially aged older than 50 years with the aid of Machine Learning (ML) models is the main purpose of this research paper. The performance of supervised ML models is compared in terms of accuracy, sensitivity (or recall) in identifying those participants that actually suffer from a CVD and Area Under Curve (AUC) score. The experimental analysis demonstrated that the Logistic Regression classifier is the most appropriate against Naive Bayes, Support Vector Machine (SVM) and Random Forest with 72.1% accuracy, recall and 78.4% AUC.

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Paper citation in several formats:
Dritsas, E.; Alexiou, S. and Moustakas, K. (2022). Cardiovascular Disease Risk Prediction with Supervised Machine Learning Techniques. In Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE; ISBN 978-989-758-566-1; ISSN 2184-4984, SciTePress, pages 315-321. DOI: 10.5220/0011088300003188

@conference{ict4awe22,
author={Elias Dritsas. and Sotiris Alexiou. and Konstantinos Moustakas.},
title={Cardiovascular Disease Risk Prediction with Supervised Machine Learning Techniques},
booktitle={Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE},
year={2022},
pages={315-321},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011088300003188},
isbn={978-989-758-566-1},
issn={2184-4984},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE
TI - Cardiovascular Disease Risk Prediction with Supervised Machine Learning Techniques
SN - 978-989-758-566-1
IS - 2184-4984
AU - Dritsas, E.
AU - Alexiou, S.
AU - Moustakas, K.
PY - 2022
SP - 315
EP - 321
DO - 10.5220/0011088300003188
PB - SciTePress

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