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

^*此作品的通信作者

醫學系內科學科

研究成果: Conference contribution › 同行評審

摘要

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.

原文	English
主出版物標題	Advanced Information Networking and Applications - Proceedings of the 36th International Conference on Advanced Information Networking and Applications AINA-2022
編輯	Leonard Barolli, Farookh Hussain, Tomoya Enokido
發行者	Springer Science and Business Media Deutschland GmbH
頁面	364-374
頁數	11
ISBN（列印）	9783030995836
DOIs	https://doi.org/10.1007/978-3-030-99584-3_32
出版狀態	Published - 2022
事件	36th International Conference on Advanced Information Networking and Applications, AINA 2022 - Sydney, 澳大利亞持續時間: 13 4月 2022 → 15 4月 2022

出版系列

名字	Lecture Notes in Networks and Systems
卷	449 LNNS
ISSN（列印）	2367-3370
ISSN（電子）	2367-3389

Conference

Conference	36th International Conference on Advanced Information Networking and Applications, AINA 2022
國家/地區	澳大利亞
城市	Sydney
期間	13/04/22 → 15/04/22

UN SDG

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

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Link to publication in Scopus

引用此

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. 於 L. Barolli, F. Hussain, & T. Enokido (編輯), Advanced Information Networking and Applications - Proceedings of the 36th International Conference on Advanced Information Networking and Applications AINA-2022 (頁 364-374). (Lecture Notes in Networks and Systems; 卷 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 等. / 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. 編輯 / Leonard Barolli ; Farookh Hussain ; Tomoya Enokido. Springer Science and Business Media Deutschland GmbH, 2022. 頁 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",

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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. 於 L Barolli, F Hussain & T Enokido (編輯), 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, 卷 449 LNNS, Springer Science and Business Media Deutschland GmbH, 頁 364-374, 36th International Conference on Advanced Information Networking and Applications, AINA 2022, Sydney, 澳大利亞, 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 等.
Advanced Information Networking and Applications - Proceedings of the 36th International Conference on Advanced Information Networking and Applications AINA-2022. 編輯 / Leonard Barolli; Farookh Hussain; Tomoya Enokido. Springer Science and Business Media Deutschland GmbH, 2022. p. 364-374 (Lecture Notes in Networks and Systems; 卷 449 LNNS).

研究成果: Conference contribution › 同行評審

TY - GEN

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

AU - Zhong, Bing Zi

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

KW - Machine learning

UR - http://www.scopus.com/inward/record.url?scp=85128669380&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-99584-3_32

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

M3 - Conference contribution

AN - SCOPUS:85128669380

SN - 9783030995836

T3 - Lecture Notes in Networks and Systems

SP - 364

EP - 374

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

A2 - Enokido, Tomoya

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 等. A Machine Learning-Based Model for Predicting the Risk of Cardiovascular Disease. 於 Barolli L, Hussain F, Enokido T, 編輯, 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

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