The Early Prediction Acute Myocardial Infarction in Real-Time Data Using an Ensemble Machine Learning Model

Bilguun Jargalsaikhan; Muhammad Saqlain; Sherazi Syed Waseem Abbas; Moon Hyun Jae; In Uk Kang; Sikandar Ali; Jong Yun Lee

doi:10.1007/978-981-13-9714-1_28

The Early Prediction Acute Myocardial Infarction in Real-Time Data Using an Ensemble Machine Learning Model

Bilguun Jargalsaikhan
, Muhammad Saqlain
, Sherazi Syed Waseem Abbas
, Moon Hyun Jae
, In Uk Kang
, Sikandar Ali
, Jong Yun Lee^*

^*Corresponding author for this work

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

7 Scopus citations

Abstract

Cardiovascular disease is one of the extremely dangerous diseases in the world. Thus, the early detection of acute myocardial infarction is a critical model for patients and doctors. If the cardiovascular disease can make early detection, patients can prevent acute myocardial infarction. In this paper, we propose a machine learning ensemble approach for early detection of cardiac events on electronic health records (EHRs). The proposed ensemble approach combines a set of different classifier algorithms that are Random Forest, Decision Tree, Artificial Neural Network, K-Nearest Neighbors, and Support Vector Machine. Data from the Korea Acute Myocardial Infarction Registry (KAMIR), real life an acute myocardial database.

Original language	English
Title of host publication	Advances in Intelligent Information Hiding and Multimedia Signal Processing - Proceedings of the 15th International Conference on IIH-MSP in conjunction with the 12th International Conference on FITAT 2019
Editors	Jeng-Shyang Pan, Jianpo Li, Pei-Wei Tsai, Lakhmi C. Jain, Lakhmi C. Jain, Lakhmi C. Jain, Lakhmi C. Jain
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	259-264
Number of pages	6
ISBN (Print)	9789811397134
DOIs	https://doi.org/10.1007/978-981-13-9714-1_28
State	Published - 2020
Externally published	Yes
Event	15th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2019 - Jilin, China Duration: 18 Jul 2019 → 20 Jul 2019

Publication series

Name	Smart Innovation, Systems and Technologies
Volume	156
ISSN (Print)	2190-3018
ISSN (Electronic)	2190-3026

Conference

Conference	15th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2019
Country/Territory	China
City	Jilin
Period	18/07/19 → 20/07/19

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Singapore Pte Ltd.

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Acute myocardial infarction
Ensemble approach
Machine learning
Risk prediction

ASJC Scopus subject areas

General Decision Sciences
General Computer Science

Access to Document

10.1007/978-981-13-9714-1_28

Cite this

Jargalsaikhan, B., Saqlain, M., Abbas, S. S. W., Jae, M. H., Kang, I. U., Ali, S., & Lee, J. Y. (2020). The Early Prediction Acute Myocardial Infarction in Real-Time Data Using an Ensemble Machine Learning Model. In J.-S. Pan, J. Li, P.-W. Tsai, L. C. Jain, L. C. Jain, L. C. Jain, & L. C. Jain (Eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing - Proceedings of the 15th International Conference on IIH-MSP in conjunction with the 12th International Conference on FITAT 2019 (pp. 259-264). (Smart Innovation, Systems and Technologies; Vol. 156). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-13-9714-1_28

Jargalsaikhan, Bilguun ; Saqlain, Muhammad ; Abbas, Sherazi Syed Waseem et al. / The Early Prediction Acute Myocardial Infarction in Real-Time Data Using an Ensemble Machine Learning Model. Advances in Intelligent Information Hiding and Multimedia Signal Processing - Proceedings of the 15th International Conference on IIH-MSP in conjunction with the 12th International Conference on FITAT 2019. editor / Jeng-Shyang Pan ; Jianpo Li ; Pei-Wei Tsai ; Lakhmi C. Jain ; Lakhmi C. Jain ; Lakhmi C. Jain ; Lakhmi C. Jain. Springer Science and Business Media Deutschland GmbH, 2020. pp. 259-264 (Smart Innovation, Systems and Technologies).

@inproceedings{40915466e60746a3ba72f870b0c02b8c,

title = "The Early Prediction Acute Myocardial Infarction in Real-Time Data Using an Ensemble Machine Learning Model",

abstract = "Cardiovascular disease is one of the extremely dangerous diseases in the world. Thus, the early detection of acute myocardial infarction is a critical model for patients and doctors. If the cardiovascular disease can make early detection, patients can prevent acute myocardial infarction. In this paper, we propose a machine learning ensemble approach for early detection of cardiac events on electronic health records (EHRs). The proposed ensemble approach combines a set of different classifier algorithms that are Random Forest, Decision Tree, Artificial Neural Network, K-Nearest Neighbors, and Support Vector Machine. Data from the Korea Acute Myocardial Infarction Registry (KAMIR), real life an acute myocardial database.",

keywords = "Acute myocardial infarction, Ensemble approach, Machine learning, Risk prediction",

author = "Bilguun Jargalsaikhan and Muhammad Saqlain and Abbas, \{Sherazi Syed Waseem\} and Jae, \{Moon Hyun\} and Kang, \{In Uk\} and Sikandar Ali and Lee, \{Jong Yun\}",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; 15th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2019 ; Conference date: 18-07-2019 Through 20-07-2019",

year = "2020",

doi = "10.1007/978-981-13-9714-1\_28",

language = "English",

isbn = "9789811397134",

series = "Smart Innovation, Systems and Technologies",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "259--264",

editor = "Jeng-Shyang Pan and Jianpo Li and Pei-Wei Tsai and Jain, \{Lakhmi C.\} and Jain, \{Lakhmi C.\} and Jain, \{Lakhmi C.\} and Jain, \{Lakhmi C.\}",

booktitle = "Advances in Intelligent Information Hiding and Multimedia Signal Processing - Proceedings of the 15th International Conference on IIH-MSP in conjunction with the 12th International Conference on FITAT 2019",

address = "Germany",

}

Jargalsaikhan, B, Saqlain, M, Abbas, SSW, Jae, MH, Kang, IU, Ali, S & Lee, JY 2020, The Early Prediction Acute Myocardial Infarction in Real-Time Data Using an Ensemble Machine Learning Model. in J-S Pan, J Li, P-W Tsai, LC Jain, LC Jain, LC Jain & LC Jain (eds), Advances in Intelligent Information Hiding and Multimedia Signal Processing - Proceedings of the 15th International Conference on IIH-MSP in conjunction with the 12th International Conference on FITAT 2019. Smart Innovation, Systems and Technologies, vol. 156, Springer Science and Business Media Deutschland GmbH, pp. 259-264, 15th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2019, Jilin, China, 18/07/19. https://doi.org/10.1007/978-981-13-9714-1_28

The Early Prediction Acute Myocardial Infarction in Real-Time Data Using an Ensemble Machine Learning Model. / Jargalsaikhan, Bilguun; Saqlain, Muhammad; Abbas, Sherazi Syed Waseem et al.
Advances in Intelligent Information Hiding and Multimedia Signal Processing - Proceedings of the 15th International Conference on IIH-MSP in conjunction with the 12th International Conference on FITAT 2019. ed. / Jeng-Shyang Pan; Jianpo Li; Pei-Wei Tsai; Lakhmi C. Jain; Lakhmi C. Jain; Lakhmi C. Jain; Lakhmi C. Jain. Springer Science and Business Media Deutschland GmbH, 2020. p. 259-264 (Smart Innovation, Systems and Technologies; Vol. 156).

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

TY - GEN

T1 - The Early Prediction Acute Myocardial Infarction in Real-Time Data Using an Ensemble Machine Learning Model

AU - Jargalsaikhan, Bilguun

AU - Saqlain, Muhammad

AU - Abbas, Sherazi Syed Waseem

AU - Jae, Moon Hyun

AU - Kang, In Uk

AU - Ali, Sikandar

AU - Lee, Jong Yun

PY - 2020

Y1 - 2020

N2 - Cardiovascular disease is one of the extremely dangerous diseases in the world. Thus, the early detection of acute myocardial infarction is a critical model for patients and doctors. If the cardiovascular disease can make early detection, patients can prevent acute myocardial infarction. In this paper, we propose a machine learning ensemble approach for early detection of cardiac events on electronic health records (EHRs). The proposed ensemble approach combines a set of different classifier algorithms that are Random Forest, Decision Tree, Artificial Neural Network, K-Nearest Neighbors, and Support Vector Machine. Data from the Korea Acute Myocardial Infarction Registry (KAMIR), real life an acute myocardial database.

AB - Cardiovascular disease is one of the extremely dangerous diseases in the world. Thus, the early detection of acute myocardial infarction is a critical model for patients and doctors. If the cardiovascular disease can make early detection, patients can prevent acute myocardial infarction. In this paper, we propose a machine learning ensemble approach for early detection of cardiac events on electronic health records (EHRs). The proposed ensemble approach combines a set of different classifier algorithms that are Random Forest, Decision Tree, Artificial Neural Network, K-Nearest Neighbors, and Support Vector Machine. Data from the Korea Acute Myocardial Infarction Registry (KAMIR), real life an acute myocardial database.

KW - Acute myocardial infarction

KW - Ensemble approach

KW - Machine learning

KW - Risk prediction

UR - https://www.scopus.com/pages/publications/85069501002

U2 - 10.1007/978-981-13-9714-1_28

DO - 10.1007/978-981-13-9714-1_28

M3 - Conference contribution

AN - SCOPUS:85069501002

SN - 9789811397134

T3 - Smart Innovation, Systems and Technologies

SP - 259

EP - 264

BT - Advances in Intelligent Information Hiding and Multimedia Signal Processing - Proceedings of the 15th International Conference on IIH-MSP in conjunction with the 12th International Conference on FITAT 2019

A2 - Pan, Jeng-Shyang

A2 - Li, Jianpo

A2 - Tsai, Pei-Wei

A2 - Jain, Lakhmi C.

PB - Springer Science and Business Media Deutschland GmbH

T2 - 15th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2019

Y2 - 18 July 2019 through 20 July 2019

ER -

Jargalsaikhan B, Saqlain M, Abbas SSW, Jae MH, Kang IU, Ali S et al. The Early Prediction Acute Myocardial Infarction in Real-Time Data Using an Ensemble Machine Learning Model. In Pan JS, Li J, Tsai PW, Jain LC, Jain LC, Jain LC, Jain LC, editors, Advances in Intelligent Information Hiding and Multimedia Signal Processing - Proceedings of the 15th International Conference on IIH-MSP in conjunction with the 12th International Conference on FITAT 2019. Springer Science and Business Media Deutschland GmbH. 2020. p. 259-264. (Smart Innovation, Systems and Technologies). doi: 10.1007/978-981-13-9714-1_28

The Early Prediction Acute Myocardial Infarction in Real-Time Data Using an Ensemble Machine Learning Model

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this