Understanding Stroke Risk Profiles in Middle-Aged Adults: A Genetic Algorithm-Based Feature Selection Aproach

Ligia Ferreira de Carvalho Gonçalves, Caio Davi Rabelo Fiorini, Daniel Rocha Franca, Marta Dias Moreira Noronha, Mark Song, Luis Enrique Zárate Galvez

2025

Abstract

Data mining and machine learning techniques have been widely used in the knowledge extraction process of medical databases, one highlight being their use to improve diagnostic systems. Decision trees are supervised black box machine learning models that, although simple, are easy to interpret. In this work, we propose the use of these techniques to describe the profile of middle-aged adults (40-59) diagnosed with stroke, a disease that in Brazil was one of the main causes of death in previous years. The genetic algorithm was applied to extract the best characteristics so that the Decision Tree algorithm could then be used in the database provided by the 2019 National Health Survey to obtain the most comprehensive rules and identify the most relevant attributes for describing the profile of these individuals. The conclusions indicate that the rules generated for middle-aged adults are mainly about routine habits, such as work or salt consumption.

Paper Citation

in Harvard Style

Gonçalves L., Fiorini C., Franca D., Noronha M., Song M. and Galvez L. (2025). Understanding Stroke Risk Profiles in Middle-Aged Adults: A Genetic Algorithm-Based Feature Selection Aproach. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-731-3, SciTePress, pages 623-630. DOI: 10.5220/0013184600003911

in Bibtex Style

@conference{healthinf25,
author={Ligia Gonçalves and Caio Fiorini and Daniel Franca and Marta Noronha and Mark Song and Luis Galvez},
title={Understanding Stroke Risk Profiles in Middle-Aged Adults: A Genetic Algorithm-Based Feature Selection Aproach},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2025},
pages={623-630},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013184600003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - Understanding Stroke Risk Profiles in Middle-Aged Adults: A Genetic Algorithm-Based Feature Selection Aproach
SN - 978-989-758-731-3
AU - Gonçalves L.
AU - Fiorini C.
AU - Franca D.
AU - Noronha M.
AU - Song M.
AU - Galvez L.
PY - 2025
SP - 623
EP - 630
DO - 10.5220/0013184600003911
PB - SciTePress