Prediction of Heart Disease and Heart Failure Using Ensemble Machine Learning Models

Abdullah Al Maruf; Aditi Golder; Abdullah Al Numan; Md Mahmudul Haque; Zeyar Aung

doi:10.1007/978-981-99-3932-9_41

Prediction of Heart Disease and Heart Failure Using Ensemble Machine Learning Models

Abdullah Al Maruf
, Aditi Golder
, Abdullah Al Numan
, Md Mahmudul Haque
, Zeyar Aung

Jahangirnagar University
Bangladesh University of Engineering and Technology

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

2 Scopus citations

Abstract

Heart disease, commonly referred to as cardiovascular disease and heart failure, has been the leading cause of mortality globally. Many risk factors for heart disease are associated with prompt access to reliable, dependable, and practical early diagnosis and disease management procedures. Identifying heart disease through early-stage signs is challenging in today’s global climate. If not caught in time, this could result in death. When there are no heart specialist doctors in remote, semi-urban, or rural areas, precise risk prediction and analysis might be critical in the early-stage identification of heart disorders. Machine learning (ML) and Deep learning (DL) approaches were employed in this study to assess massive volumes of complex medical data, supporting specialists in predicting heart illness and mortality from heart failure. This study used two datasets: one to forecast heart disease and the other to analyze and forecast death due to heart failure. Predicting cardiac illnesses using Artificial Neural Networks is 91.52% accurate (ANN). The bagging ensemble predicted heart failure with 90% accuracy. The primary contribution of this research is an ensemble strategy with high performance that multiple measurements have demonstrated to predict heart failure and cardiac disorders using ANN.

Original language	British English
Title of host publication	Intelligent Systems - Proceedings of 3rd International Conference on Machine Learning, IoT and Big Data ICMIB 2023
Editors	Siba K. Udgata, Srinivas Sethi, Xiao-Zhi Gao
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	481-492
Number of pages	12
ISBN (Print)	9789819939312
DOIs	https://doi.org/10.1007/978-981-99-3932-9_41
State	Published - 2024
Event	3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023 - Sarang, India Duration: 10 Mar 2023 → 12 Mar 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	728 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023
Country/Territory	India
City	Sarang
Period	10/03/23 → 12/03/23

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Artificial Neural Network
Ensemble
Healthcare
Heart Disease
Heart Failure
Machine Learning

Access to Document

10.1007/978-981-99-3932-9_41

OpenUrl availability

Full text

Cite this

Al Maruf, A., Golder, A., Al Numan, A., Haque, M. M., & Aung, Z. (2024). Prediction of Heart Disease and Heart Failure Using Ensemble Machine Learning Models. In S. K. Udgata, S. Sethi, & X.-Z. Gao (Eds.), Intelligent Systems - Proceedings of 3rd International Conference on Machine Learning, IoT and Big Data ICMIB 2023 (pp. 481-492). (Lecture Notes in Networks and Systems; Vol. 728 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-3932-9_41

Al Maruf, Abdullah ; Golder, Aditi ; Al Numan, Abdullah et al. / Prediction of Heart Disease and Heart Failure Using Ensemble Machine Learning Models. Intelligent Systems - Proceedings of 3rd International Conference on Machine Learning, IoT and Big Data ICMIB 2023. editor / Siba K. Udgata ; Srinivas Sethi ; Xiao-Zhi Gao. Springer Science and Business Media Deutschland GmbH, 2024. pp. 481-492 (Lecture Notes in Networks and Systems).

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abstract = "Heart disease, commonly referred to as cardiovascular disease and heart failure, has been the leading cause of mortality globally. Many risk factors for heart disease are associated with prompt access to reliable, dependable, and practical early diagnosis and disease management procedures. Identifying heart disease through early-stage signs is challenging in today{\textquoteright}s global climate. If not caught in time, this could result in death. When there are no heart specialist doctors in remote, semi-urban, or rural areas, precise risk prediction and analysis might be critical in the early-stage identification of heart disorders. Machine learning (ML) and Deep learning (DL) approaches were employed in this study to assess massive volumes of complex medical data, supporting specialists in predicting heart illness and mortality from heart failure. This study used two datasets: one to forecast heart disease and the other to analyze and forecast death due to heart failure. Predicting cardiac illnesses using Artificial Neural Networks is 91.52\% accurate (ANN). The bagging ensemble predicted heart failure with 90\% accuracy. The primary contribution of this research is an ensemble strategy with high performance that multiple measurements have demonstrated to predict heart failure and cardiac disorders using ANN.",

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Al Maruf, A, Golder, A, Al Numan, A, Haque, MM & Aung, Z 2024, Prediction of Heart Disease and Heart Failure Using Ensemble Machine Learning Models. in SK Udgata, S Sethi & X-Z Gao (eds), Intelligent Systems - Proceedings of 3rd International Conference on Machine Learning, IoT and Big Data ICMIB 2023. Lecture Notes in Networks and Systems, vol. 728 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 481-492, 3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023, Sarang, India, 10/03/23. https://doi.org/10.1007/978-981-99-3932-9_41

Prediction of Heart Disease and Heart Failure Using Ensemble Machine Learning Models. / Al Maruf, Abdullah; Golder, Aditi; Al Numan, Abdullah et al.
Intelligent Systems - Proceedings of 3rd International Conference on Machine Learning, IoT and Big Data ICMIB 2023. ed. / Siba K. Udgata; Srinivas Sethi; Xiao-Zhi Gao. Springer Science and Business Media Deutschland GmbH, 2024. p. 481-492 (Lecture Notes in Networks and Systems; Vol. 728 LNNS).

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

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N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

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Al Maruf A, Golder A, Al Numan A, Haque MM, Aung Z. Prediction of Heart Disease and Heart Failure Using Ensemble Machine Learning Models. In Udgata SK, Sethi S, Gao XZ, editors, Intelligent Systems - Proceedings of 3rd International Conference on Machine Learning, IoT and Big Data ICMIB 2023. Springer Science and Business Media Deutschland GmbH. 2024. p. 481-492. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-99-3932-9_41