research-article

Random forest swarm optimization-based for heart diseases diagnosis

Authors:

Shahrokh Asadi,

SeyedEhsan Roshan,

Michael W. KattanAuthors Info & Claims

Volume 115, Issue C

https://doi.org/10.1016/j.jbi.2021.103690

Published: 01 March 2021 Publication History

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Highlights

•

Through combining the multi-objective particle swarm optimization and Random forest, a new approach is proposed to predict the heart disease.

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The main goal is to produce diverse and accurate classifiers and determine the (near) optimal number of classifiers.

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The results indicate that the proposed algorithm outperforms the other techniques in terms of accuracy and statistical tests.

Abstract

Heart disease has been one of the leading causes of death worldwide in recent years. Among diagnostic methods for heart disease, angiography is one of the most common methods, but it is costly and has side effects. Given the difficulty of heart disease prediction, data mining can play an important role in predicting heart disease accurately. In this paper, by combining the multi-objective particle swarm optimization (MOPSO) and Random Forest, a new approach is proposed to predict heart disease. The main goal is to produce diverse and accurate decision trees and determine the (near) optimal number of them simultaneously. In this method, an evolutionary multi-objective approach is used instead of employing a commonly used approach, i.e., bootstrap, feature selection in the Random Forest, and random number selection of training sets. By doing so, different training sets with different samples and features for training each tree are generated. Also, the obtained solutions in Pareto-optimal fronts determine the required number of training sets to build the random forest. By doing so, the random forest's performance can be enhanced, and consequently, the prediction accuracy will be improved. The proposed method's effectiveness is investigated by comparing its performance over six heart datasets with individual and ensemble classifiers. The results suggest that the proposed method with the (near) optimal number of classifiers outperforms the random forest algorithm with different classifiers.

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Random forest swarm optimization-based for heart diseases diagnosis

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cover image Journal of Biomedical Informatics

Journal of Biomedical Informatics Volume 115, Issue C

Mar 2021

228 pages

ISSN:1532-0464

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Elsevier Inc.

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Elsevier Science

San Diego, CA, United States

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Published: 01 March 2021

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Cited By

Vigneshvaran PKathiravan A(2024)Optimized Extreme Learning Machine with Bacterial Colony Optimization Algorithm for Disease Diagnosis in Clinical DatasetsSN Computer Science10.1007/s42979-024-02864-85:5Online publication date: 26-May-2024
https://dl.acm.org/doi/10.1007/s42979-024-02864-8
Zang XDu JSong Y(2023)Early Prediction of Heart Disease via LSTM-XGBoostProceedings of the 2023 9th International Conference on Computing and Artificial Intelligence10.1145/3594315.3594383(631-637)Online publication date: 17-Mar-2023
https://dl.acm.org/doi/10.1145/3594315.3594383
Tahir AChen FAlmazroi AJanbi N(2023)SWEP-RFJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.10167235:8Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.jksuci.2023.101672
Kıymaç EKaya Y(2023)A novel automated CNN arrhythmia classifier with memory-enhanced artificial hummingbird algorithmExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119162213:PCOnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119162

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