A Machine Learning-Based Model for Predicting the Risk of Cardiovascular Disease

Chiu Han Hsiao; Po Chun Yu; Chia Ying Hsieh; Bing Zi Zhong; Yu Ling Tsai; Hao min Cheng; Wei Lun Chang; Frank Yeong Sung Lin; Yennun Huang

doi:10.1007/978-3-030-99584-3_32

A Machine Learning-Based Model for Predicting the Risk of Cardiovascular Disease

Chiu Han Hsiao^*, Po Chun Yu, Chia Ying Hsieh, Bing Zi Zhong, Yu Ling Tsai, Hao min Cheng, Wei Lun Chang, Frank Yeong Sung Lin, Yennun Huang

^*Corresponding author for this work

Department of Internal Medicine

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

Abstract

A growing number of medical studies have used deep learning and machine learning for the modeling and early prediction of cardiovascular disease (CVD) risk. Modern hospitals have constructed sizeable medical data sets to predict abnormal blood pressure (BP), abnormal heart vessels, and other cardiac indicators. However, hypertension has also been demonstrated to be a risk factor for cardiovascular disease and stroke. In this paper, machine learning-based and statistic-based approaches were applied to medical data to significantly identify the disease to prevent serious illness. Furthermore, lightweight BP monitoring devices that can be used at home have enabled regular BP monitoring to predict CVD risks for early treatment.

Original language	English
Title of host publication	Advanced Information Networking and Applications - Proceedings of the 36th International Conference on Advanced Information Networking and Applications AINA-2022
Editors	Leonard Barolli, Farookh Hussain, Tomoya Enokido
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	364-374
Number of pages	11
ISBN (Print)	9783030995836
DOIs	https://doi.org/10.1007/978-3-030-99584-3_32
State	Published - 2022
Event	36th International Conference on Advanced Information Networking and Applications, AINA 2022 - Sydney, Australia Duration: 13 Apr 2022 → 15 Apr 2022

Publication series

Name	Lecture Notes in Networks and Systems
Volume	449 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	36th International Conference on Advanced Information Networking and Applications, AINA 2022
Country/Territory	Australia
City	Sydney
Period	13/04/22 → 15/04/22

Keywords

Artificial intelligence
Cardiovascular disease
Hypertension
Machine learning

UN SDGs

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

Access to Document

10.1007/978-3-030-99584-3_32

Cite this

Hsiao, C. H., Yu, P. C., Hsieh, C. Y., Zhong, B. Z., Tsai, Y. L., Cheng, H. M., Chang, W. L., Lin, F. Y. S., & Huang, Y. (2022). A Machine Learning-Based Model for Predicting the Risk of Cardiovascular Disease. In L. Barolli, F. Hussain, & T. Enokido (Eds.), Advanced Information Networking and Applications - Proceedings of the 36th International Conference on Advanced Information Networking and Applications AINA-2022 (pp. 364-374). (Lecture Notes in Networks and Systems; Vol. 449 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-99584-3_32

Hsiao, Chiu Han ; Yu, Po Chun ; Hsieh, Chia Ying et al. / A Machine Learning-Based Model for Predicting the Risk of Cardiovascular Disease. Advanced Information Networking and Applications - Proceedings of the 36th International Conference on Advanced Information Networking and Applications AINA-2022. editor / Leonard Barolli ; Farookh Hussain ; Tomoya Enokido. Springer Science and Business Media Deutschland GmbH, 2022. pp. 364-374 (Lecture Notes in Networks and Systems).

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title = "A Machine Learning-Based Model for Predicting the Risk of Cardiovascular Disease",

abstract = "A growing number of medical studies have used deep learning and machine learning for the modeling and early prediction of cardiovascular disease (CVD) risk. Modern hospitals have constructed sizeable medical data sets to predict abnormal blood pressure (BP), abnormal heart vessels, and other cardiac indicators. However, hypertension has also been demonstrated to be a risk factor for cardiovascular disease and stroke. In this paper, machine learning-based and statistic-based approaches were applied to medical data to significantly identify the disease to prevent serious illness. Furthermore, lightweight BP monitoring devices that can be used at home have enabled regular BP monitoring to predict CVD risks for early treatment.",

keywords = "Artificial intelligence, Cardiovascular disease, Hypertension, Machine learning",

author = "Hsiao, {Chiu Han} and Yu, {Po Chun} and Hsieh, {Chia Ying} and Zhong, {Bing Zi} and Tsai, {Yu Ling} and Cheng, {Hao min} and Chang, {Wei Lun} and Lin, {Frank Yeong Sung} and Yennun Huang",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 36th International Conference on Advanced Information Networking and Applications, AINA 2022 ; Conference date: 13-04-2022 Through 15-04-2022",

year = "2022",

doi = "10.1007/978-3-030-99584-3_32",

language = "English",

isbn = "9783030995836",

series = "Lecture Notes in Networks and Systems",

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

pages = "364--374",

editor = "Leonard Barolli and Farookh Hussain and Tomoya Enokido",

booktitle = "Advanced Information Networking and Applications - Proceedings of the 36th International Conference on Advanced Information Networking and Applications AINA-2022",

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}

Hsiao, CH, Yu, PC, Hsieh, CY, Zhong, BZ, Tsai, YL, Cheng, HM, Chang, WL, Lin, FYS & Huang, Y 2022, A Machine Learning-Based Model for Predicting the Risk of Cardiovascular Disease. in L Barolli, F Hussain & T Enokido (eds), Advanced Information Networking and Applications - Proceedings of the 36th International Conference on Advanced Information Networking and Applications AINA-2022. Lecture Notes in Networks and Systems, vol. 449 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 364-374, 36th International Conference on Advanced Information Networking and Applications, AINA 2022, Sydney, Australia, 13/04/22. https://doi.org/10.1007/978-3-030-99584-3_32

A Machine Learning-Based Model for Predicting the Risk of Cardiovascular Disease. / Hsiao, Chiu Han; Yu, Po Chun; Hsieh, Chia Ying et al.
Advanced Information Networking and Applications - Proceedings of the 36th International Conference on Advanced Information Networking and Applications AINA-2022. ed. / Leonard Barolli; Farookh Hussain; Tomoya Enokido. Springer Science and Business Media Deutschland GmbH, 2022. p. 364-374 (Lecture Notes in Networks and Systems; Vol. 449 LNNS).

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

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T1 - A Machine Learning-Based Model for Predicting the Risk of Cardiovascular Disease

AU - Hsiao, Chiu Han

AU - Yu, Po Chun

AU - Hsieh, Chia Ying

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AU - Tsai, Yu Ling

AU - Cheng, Hao min

AU - Chang, Wei Lun

AU - Lin, Frank Yeong Sung

AU - Huang, Yennun

PY - 2022

Y1 - 2022

N2 - A growing number of medical studies have used deep learning and machine learning for the modeling and early prediction of cardiovascular disease (CVD) risk. Modern hospitals have constructed sizeable medical data sets to predict abnormal blood pressure (BP), abnormal heart vessels, and other cardiac indicators. However, hypertension has also been demonstrated to be a risk factor for cardiovascular disease and stroke. In this paper, machine learning-based and statistic-based approaches were applied to medical data to significantly identify the disease to prevent serious illness. Furthermore, lightweight BP monitoring devices that can be used at home have enabled regular BP monitoring to predict CVD risks for early treatment.

AB - A growing number of medical studies have used deep learning and machine learning for the modeling and early prediction of cardiovascular disease (CVD) risk. Modern hospitals have constructed sizeable medical data sets to predict abnormal blood pressure (BP), abnormal heart vessels, and other cardiac indicators. However, hypertension has also been demonstrated to be a risk factor for cardiovascular disease and stroke. In this paper, machine learning-based and statistic-based approaches were applied to medical data to significantly identify the disease to prevent serious illness. Furthermore, lightweight BP monitoring devices that can be used at home have enabled regular BP monitoring to predict CVD risks for early treatment.

KW - Artificial intelligence

KW - Cardiovascular disease

KW - Hypertension

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M3 - Conference contribution

AN - SCOPUS:85128669380

SN - 9783030995836

T3 - Lecture Notes in Networks and Systems

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BT - Advanced Information Networking and Applications - Proceedings of the 36th International Conference on Advanced Information Networking and Applications AINA-2022

A2 - Barolli, Leonard

A2 - Hussain, Farookh

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PB - Springer Science and Business Media Deutschland GmbH

T2 - 36th International Conference on Advanced Information Networking and Applications, AINA 2022

Y2 - 13 April 2022 through 15 April 2022

ER -

Hsiao CH, Yu PC, Hsieh CY, Zhong BZ, Tsai YL, Cheng HM et al. A Machine Learning-Based Model for Predicting the Risk of Cardiovascular Disease. In Barolli L, Hussain F, Enokido T, editors, Advanced Information Networking and Applications - Proceedings of the 36th International Conference on Advanced Information Networking and Applications AINA-2022. Springer Science and Business Media Deutschland GmbH. 2022. p. 364-374. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-030-99584-3_32

A Machine Learning-Based Model for Predicting the Risk of Cardiovascular Disease

Abstract

Publication series

Conference

Keywords

UN SDGs

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