Ensemble Feature Selection for Heart Disease Classification

Houda Benhar, Ali Idri, Ali Idri, Mohamed Hosni, Mohamed Hosni

2022

Abstract

Feature selection is a fundamental data preparation task in any data mining objective. Deciding on the best feature selection technique to use for a specific context is difficult and time-consuming. Ensemble learning can alleviate this issue. Ensemble methods are based on the assumption that the aggregate results of a group of experts with average knowledge can often be superior to those of highly knowledgeable individual ones. The present study aims to propose a heterogeneous ensemble feature selection for heart disease classification. The proposed ensembles were constructed by combining the results of five univariate filter feature selection techniques using two aggregation methods. The performance of the proposed techniques was evaluated with four classifiers and six heart disease datasets. The empirical experiments showed that applying ensemble feature ranking produced very promising results compared to single ones and previous studies.

Paper Citation

in Harvard Style

Benhar H., Idri A. and Hosni M. (2022). Ensemble Feature Selection for Heart Disease Classification. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF; ISBN 978-989-758-552-4, SciTePress, pages 369-376. DOI: 10.5220/0010800500003123

in Bibtex Style

@conference{healthinf22,
author={Houda Benhar and Ali Idri and Mohamed Hosni},
title={Ensemble Feature Selection for Heart Disease Classification},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF},
year={2022},
pages={369-376},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010800500003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF
TI - Ensemble Feature Selection for Heart Disease Classification
SN - 978-989-758-552-4
AU - Benhar H.
AU - Idri A.
AU - Hosni M.
PY - 2022
SP - 369
EP - 376
DO - 10.5220/0010800500003123
PB - SciTePress