A Knowledge Discovery Pipeline to Describe the High Cholesterol Profile in Young People Using GA for Feature Selection

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

2025

Abstract

Understanding the risk factors associated with hypercholesterolemia in young individuals is crucial for developing preventive strategies to combat cardiovascular diseases. This study proposes a data mining pipeline employing machine learning techniques to profile high cholesterol in Brazilian youth aged 15 to 25, utilizing the 2019 National Health Survey (PNS) dataset. The PNS-2019 database has 1,088 attributes organized into 26 modules and 293,726 anonymized records. The Knowledge Discovery in Databases (KDD) process was implemented, incorporating a novel CAPTO-based conceptual attribute selection followed by feature selection using a Non-dominated Sorting Genetic Algorithm II (NSGA-II). A decision tree classifier was optimized and evaluated, achieving an F1 Score of 66%, demonstrating reasonable predictive power despite data limitations. The results highlight the significant impact of dietary habits, particularly high sugar and fat intake, on hyper-cholesterolemia risk. The study emphasizes the potential for early identification and targeted interventions, contributing to public health improvements and laying the groundwork for future research with advanced models and additional data sources.

Paper Citation

in Harvard Style

Franca D., Fiorini C., Gonçalves L., Noronha M., Song M. and Galvez L. (2025). A Knowledge Discovery Pipeline to Describe the High Cholesterol Profile in Young People Using GA for Feature Selection. In Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-749-8, SciTePress, pages 805-812. DOI: 10.5220/0013294800003929

in Bibtex Style

@conference{iceis25,
author={Daniel Franca and Caio Fiorini and Ligia Gonçalves and Marta Noronha and Mark Song and Luis Galvez},
title={A Knowledge Discovery Pipeline to Describe the High Cholesterol Profile in Young People Using GA for Feature Selection},
booktitle={Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2025},
pages={805-812},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013294800003929},
isbn={978-989-758-749-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - A Knowledge Discovery Pipeline to Describe the High Cholesterol Profile in Young People Using GA for Feature Selection
SN - 978-989-758-749-8
AU - Franca D.
AU - Fiorini C.
AU - Gonçalves L.
AU - Noronha M.
AU - Song M.
AU - Galvez L.
PY - 2025
SP - 805
EP - 812
DO - 10.5220/0013294800003929
PB - SciTePress