Comparing Ensemble and Single Classifiers Using KNN Imputation for Incomplete Heart Disease Datasets

Ismail Moatadid, Ibtissam Abnane, Ali Idri

2023

Abstract

Heart disease remains a significant global health challenge, necessitating accurate and reliable classification techniques for early detection and diagnosis. Choosing a suitable classifier model for a dataset containing missing data is a pervasive issue in medical datasets, which can severely impact the performance of classification models. In this work, we present a comparative analysis of three ensemble techniques (i.e. Random Forest (RF), Extreme Gradient Boosting (XGB), and Bagging) and three single technique (i.e. K-nearest neighbor (KNN), Multilayer Perceptron (MLP), and Support Vector Machine (SVM)) applied to four heart disease medical datasets (i.e. Hungarian, Cleveland, Statlog and HeartDisease). The main objective of this study is to compare the performance of ensemble and single classifiers in handling incomplete heart disease datasets using KNN imputation and identify an effective approach for heart disease classification. We found that, overall, MLP outperformed SVM and KNN across datasets. Moreover, we found that ensemble techniques consistently outperformed the single techniques across multiple metrics and datasets. The ensemble models consistently achieved higher accuracy, precision, recall, F1 score, and AUC values. Therefore, for heart disease classification using KNN imputation, the ensemble techniques, particularly RF, Bagging, and XGB, proved to be the most effective models.

Paper Citation

in Harvard Style

Moatadid I., Abnane I. and Idri A. (2023). Comparing Ensemble and Single Classifiers Using KNN Imputation for Incomplete Heart Disease Datasets. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-671-2, SciTePress, pages 379-386. DOI: 10.5220/0012208300003598

in Bibtex Style

@conference{kdir23,
author={Ismail Moatadid and Ibtissam Abnane and Ali Idri},
title={Comparing Ensemble and Single Classifiers Using KNN Imputation for Incomplete Heart Disease Datasets},
booktitle={Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2023},
pages={379-386},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012208300003598},
isbn={978-989-758-671-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Comparing Ensemble and Single Classifiers Using KNN Imputation for Incomplete Heart Disease Datasets
SN - 978-989-758-671-2
AU - Moatadid I.
AU - Abnane I.
AU - Idri A.
PY - 2023
SP - 379
EP - 386
DO - 10.5220/0012208300003598
PB - SciTePress