Utilizing Optimization Techniques in Feature Selection for Effective Cardiovascular Disease Prediction

Aneesa Al Redhaei; Wafa'Abu A. Fares; Mohammed Azmi Al Betar

doi:10.1109/ACIT58888.2023.10453772

Utilizing Optimization Techniques in Feature Selection for Effective Cardiovascular Disease Prediction

Aneesa Al Redhaei
, Wafa'Abu A. Fares
, Mohammed Azmi Al Betar

College of Engineering and Information Technology

Ajman University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Cardiovascular disease (CVD) is considered one of the significant global health challenges, affecting individuals and societies worldwide. This paper aims to address the challenge of predicting CVD by developing a prediction model based on optimal feature selection. The process begins with data preprocessing of the Z-Alizadeh Sani dataset. Three optimization algorithms were utilized: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Arithmetic Optimization Algorithm (AOA) to find the most important and relevant features to improve the heart disease prediction accuracy of some selected popular machine learning classifiers. Simulation results showed that the proposed work achieves 98.59 % of accuracy.

Original language	English
Title of host publication	2023 24th International Arab Conference on Information Technology, ACIT 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350384307
DOIs	https://doi.org/10.1109/ACIT58888.2023.10453772
State	Published - 2023
Event	24th International Arab Conference on Information Technology, ACIT 2023 - Ajman, United Arab Emirates Duration: 6 Dec 2023 → 8 Dec 2023

Publication series

Name	2023 24th International Arab Conference on Information Technology, ACIT 2023

Conference

Conference	24th International Arab Conference on Information Technology, ACIT 2023
Country/Territory	United Arab Emirates
City	Ajman
Period	6/12/23 → 8/12/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Arithmetic Optimization Algorithm
Feature Selection
Genetic Algorithm
Machine Learning
Particle Swarm Optimization

Access to Document

10.1109/ACIT58888.2023.10453772

Cite this

Al Redhaei, A., Fares, WA. A., & Al Betar, M. A. (2023). Utilizing Optimization Techniques in Feature Selection for Effective Cardiovascular Disease Prediction. In 2023 24th International Arab Conference on Information Technology, ACIT 2023 (2023 24th International Arab Conference on Information Technology, ACIT 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACIT58888.2023.10453772

Al Redhaei, Aneesa ; Fares, Wafa'Abu A. ; Al Betar, Mohammed Azmi. / Utilizing Optimization Techniques in Feature Selection for Effective Cardiovascular Disease Prediction. 2023 24th International Arab Conference on Information Technology, ACIT 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 24th International Arab Conference on Information Technology, ACIT 2023).

@inproceedings{b9b8071fc8354fc385c80dd2849a0e97,

title = "Utilizing Optimization Techniques in Feature Selection for Effective Cardiovascular Disease Prediction",

abstract = "Cardiovascular disease (CVD) is considered one of the significant global health challenges, affecting individuals and societies worldwide. This paper aims to address the challenge of predicting CVD by developing a prediction model based on optimal feature selection. The process begins with data preprocessing of the Z-Alizadeh Sani dataset. Three optimization algorithms were utilized: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Arithmetic Optimization Algorithm (AOA) to find the most important and relevant features to improve the heart disease prediction accuracy of some selected popular machine learning classifiers. Simulation results showed that the proposed work achieves 98.59 \% of accuracy.",

keywords = "Arithmetic Optimization Algorithm, Feature Selection, Genetic Algorithm, Machine Learning, Particle Swarm Optimization",

author = "\{Al Redhaei\}, Aneesa and Fares, \{Wafa'Abu A.\} and \{Al Betar\}, \{Mohammed Azmi\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 24th International Arab Conference on Information Technology, ACIT 2023 ; Conference date: 06-12-2023 Through 08-12-2023",

year = "2023",

doi = "10.1109/ACIT58888.2023.10453772",

language = "English",

series = "2023 24th International Arab Conference on Information Technology, ACIT 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2023 24th International Arab Conference on Information Technology, ACIT 2023",

address = "United States",

}

Al Redhaei, A, Fares, WAA & Al Betar, MA 2023, Utilizing Optimization Techniques in Feature Selection for Effective Cardiovascular Disease Prediction. in 2023 24th International Arab Conference on Information Technology, ACIT 2023. 2023 24th International Arab Conference on Information Technology, ACIT 2023, Institute of Electrical and Electronics Engineers Inc., 24th International Arab Conference on Information Technology, ACIT 2023, Ajman, United Arab Emirates, 6/12/23. https://doi.org/10.1109/ACIT58888.2023.10453772

Utilizing Optimization Techniques in Feature Selection for Effective Cardiovascular Disease Prediction. / Al Redhaei, Aneesa; Fares, Wafa'Abu A.; Al Betar, Mohammed Azmi.
2023 24th International Arab Conference on Information Technology, ACIT 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 24th International Arab Conference on Information Technology, ACIT 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Utilizing Optimization Techniques in Feature Selection for Effective Cardiovascular Disease Prediction

AU - Al Redhaei, Aneesa

AU - Fares, Wafa'Abu A.

AU - Al Betar, Mohammed Azmi

PY - 2023

Y1 - 2023

N2 - Cardiovascular disease (CVD) is considered one of the significant global health challenges, affecting individuals and societies worldwide. This paper aims to address the challenge of predicting CVD by developing a prediction model based on optimal feature selection. The process begins with data preprocessing of the Z-Alizadeh Sani dataset. Three optimization algorithms were utilized: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Arithmetic Optimization Algorithm (AOA) to find the most important and relevant features to improve the heart disease prediction accuracy of some selected popular machine learning classifiers. Simulation results showed that the proposed work achieves 98.59 % of accuracy.

AB - Cardiovascular disease (CVD) is considered one of the significant global health challenges, affecting individuals and societies worldwide. This paper aims to address the challenge of predicting CVD by developing a prediction model based on optimal feature selection. The process begins with data preprocessing of the Z-Alizadeh Sani dataset. Three optimization algorithms were utilized: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Arithmetic Optimization Algorithm (AOA) to find the most important and relevant features to improve the heart disease prediction accuracy of some selected popular machine learning classifiers. Simulation results showed that the proposed work achieves 98.59 % of accuracy.

KW - Arithmetic Optimization Algorithm

KW - Feature Selection

KW - Genetic Algorithm

KW - Machine Learning

KW - Particle Swarm Optimization

UR - https://www.scopus.com/pages/publications/85189134964

U2 - 10.1109/ACIT58888.2023.10453772

DO - 10.1109/ACIT58888.2023.10453772

M3 - Conference contribution

AN - SCOPUS:85189134964

T3 - 2023 24th International Arab Conference on Information Technology, ACIT 2023

BT - 2023 24th International Arab Conference on Information Technology, ACIT 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 24th International Arab Conference on Information Technology, ACIT 2023

Y2 - 6 December 2023 through 8 December 2023

ER -

Al Redhaei A, Fares WAA, Al Betar MA. Utilizing Optimization Techniques in Feature Selection for Effective Cardiovascular Disease Prediction. In 2023 24th International Arab Conference on Information Technology, ACIT 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 24th International Arab Conference on Information Technology, ACIT 2023). doi: 10.1109/ACIT58888.2023.10453772

Utilizing Optimization Techniques in Feature Selection for Effective Cardiovascular Disease Prediction

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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