Metaheuristics Applied to Optimal Feature Selection for Accurate Predictive Models in Smart Health: A Case Study on Hypotension Prediction in Hemodialysis Patients

María Santamera-Lastras, María Santamera-Lastras, Felipe Cisternas Caneo, José Carlos Barrera García, Broderick Crawford, Alberto Garcéz-Jiménez, Alberto Garcéz-Jiménez, Alberto Garcéz-Jiménez, Diego Rodríguez Puyol, Diego Rodríguez Puyol, José Manuel Gómez Pulido, José Manuel Gómez Pulido, José Manuel Gómez Pulido

2025

Abstract

Predicting potential hypotensive episodes in chronic kidney disease patients before dialysis is crucial for preventing complications and ensuring effective treatment. This study explores the use of metaheuristic algorithms to optimize the complex task of selecting the feature set needed to develop a highly accurate predictive machine learning model for detecting hypotension, based on clinical parameters from the dialyzer and analytical data from blood tests. Metaheuristic algorithms offer a robust approach to optimal variable selection and subsequent dimensionality reduction, leading to more accurate machine learning predictor models. In this context, two relevant metaheuristic algorithms were employed: Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO), along with the supervised machine learning algorithm XGBoost. The results demonstrate that the application of metaheuristic techniques not only reduces the feature count from 67 to 36 variables but also improves classifier performance, thereby enhancing the prediction of hypotensive events. Specifically, the optimized model achieved an Area Under the Curve (AUC) of 0.76 and a recall of 0.764 for the minority class (hypotensive episodes) in chronic kidney disease patients prior to hemodialysis procedures.

Paper Citation

in Harvard Style

Santamera-Lastras M., Cisternas Caneo F., Barrera García J., Crawford B., Garcéz-Jiménez A., Rodríguez Puyol D. and Gómez Pulido J. (2025). Metaheuristics Applied to Optimal Feature Selection for Accurate Predictive Models in Smart Health: A Case Study on Hypotension Prediction in Hemodialysis Patients. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS; ISBN 978-989-758-731-3, SciTePress, pages 563-570. DOI: 10.5220/0013164000003911

in Bibtex Style

@conference{bioinformatics25,
author={María Santamera-Lastras and Felipe Cisternas Caneo and José Carlos Barrera García and Broderick Crawford and Alberto Garcéz-Jiménez and Diego Rodríguez Puyol and José Manuel Gómez Pulido},
title={Metaheuristics Applied to Optimal Feature Selection for Accurate Predictive Models in Smart Health: A Case Study on Hypotension Prediction in Hemodialysis Patients},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS},
year={2025},
pages={563-570},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013164000003911},
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 1: BIOINFORMATICS
TI - Metaheuristics Applied to Optimal Feature Selection for Accurate Predictive Models in Smart Health: A Case Study on Hypotension Prediction in Hemodialysis Patients
SN - 978-989-758-731-3
AU - Santamera-Lastras M.
AU - Cisternas Caneo F.
AU - Barrera García J.
AU - Crawford B.
AU - Garcéz-Jiménez A.
AU - Rodríguez Puyol D.
AU - Gómez Pulido J.
PY - 2025
SP - 563
EP - 570
DO - 10.5220/0013164000003911
PB - SciTePress