Machine Learning-Based Predictive Models for Early Detection of Cardiovascular Diseases: A Study Utilizing Patient Samples from a Tertiary Health Promotion Center in Korea

Kanghyuck Lee; Seol Whan Oh; Sung Hwan Kim; Taehoon Ko; In Young Choi

doi:10.3233/SHTI240512

Machine Learning-Based Predictive Models for Early Detection of Cardiovascular Diseases: A Study Utilizing Patient Samples from a Tertiary Health Promotion Center in Korea

Kanghyuck Lee
, Seol Whan Oh
, Sung Hwan Kim
, Taehoon Ko
, In Young Choi

The Catholic University of Korea, College of Medicine

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

Abstract

A machine learning model was developed for cardiovascular diseases prediction based on 21,118 patient checkups data from a tertiary medical institution in Seoul, Korea, collected between 2009 and 2021. XGBoost algorithm showed the highest predictive performance, with an average AUROC of 0.877. In survival analysis, XGBSE achieved an AUROC exceeding 0.9 for 2-9 year predictions, with a C-index of 0.878 across all diseases, outperforming Cox regression (C-index of 0.887). A high-performance prediction model for cardiovascular diseases using the XGBSE algorithm was successfully developed and is poised for real-world clinical application following external simplification and validation.

Original language	English
Title of host publication	Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024
Editors	John Mantas, Arie Hasman, George Demiris, Kaija Saranto, Michael Marschollek, Theodoros N. Arvanitis, Ivana Ognjanovic, Arriel Benis, Parisis Gallos, Emmanouil Zoulias, Elisavet Andrikopoulou
Publisher	IOS Press BV
Pages	710-711
Number of pages	2
ISBN (Electronic)	9781643685335
DOIs	https://doi.org/10.3233/SHTI240512
State	Published - 22 Aug 2024
Event	34th Medical Informatics Europe Conference, MIE 2024 - Athens, Greece Duration: 25 Aug 2024 → 29 Aug 2024

Publication series

Name	Studies in Health Technology and Informatics
Volume	316
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	34th Medical Informatics Europe Conference, MIE 2024
Country/Territory	Greece
City	Athens
Period	25/08/24 → 29/08/24

Bibliographical note

Publisher Copyright:
© 2024 The Authors.

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Artificial Intelligence
Electronic Health Records
Survival Analysis

Access to Document

10.3233/SHTI240512

Cite this

Lee, K., Oh, S. W., Kim, S. H., Ko, T., & Choi, I. Y. (2024). Machine Learning-Based Predictive Models for Early Detection of Cardiovascular Diseases: A Study Utilizing Patient Samples from a Tertiary Health Promotion Center in Korea. In J. Mantas, A. Hasman, G. Demiris, K. Saranto, M. Marschollek, T. N. Arvanitis, I. Ognjanovic, A. Benis, P. Gallos, E. Zoulias, & E. Andrikopoulou (Eds.), Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024 (pp. 710-711). (Studies in Health Technology and Informatics; Vol. 316). IOS Press BV. https://doi.org/10.3233/SHTI240512

Lee, Kanghyuck ; Oh, Seol Whan ; Kim, Sung Hwan et al. / Machine Learning-Based Predictive Models for Early Detection of Cardiovascular Diseases : A Study Utilizing Patient Samples from a Tertiary Health Promotion Center in Korea. Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024. editor / John Mantas ; Arie Hasman ; George Demiris ; Kaija Saranto ; Michael Marschollek ; Theodoros N. Arvanitis ; Ivana Ognjanovic ; Arriel Benis ; Parisis Gallos ; Emmanouil Zoulias ; Elisavet Andrikopoulou. IOS Press BV, 2024. pp. 710-711 (Studies in Health Technology and Informatics).

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title = "Machine Learning-Based Predictive Models for Early Detection of Cardiovascular Diseases: A Study Utilizing Patient Samples from a Tertiary Health Promotion Center in Korea",

abstract = "A machine learning model was developed for cardiovascular diseases prediction based on 21,118 patient checkups data from a tertiary medical institution in Seoul, Korea, collected between 2009 and 2021. XGBoost algorithm showed the highest predictive performance, with an average AUROC of 0.877. In survival analysis, XGBSE achieved an AUROC exceeding 0.9 for 2-9 year predictions, with a C-index of 0.878 across all diseases, outperforming Cox regression (C-index of 0.887). A high-performance prediction model for cardiovascular diseases using the XGBSE algorithm was successfully developed and is poised for real-world clinical application following external simplification and validation.",

keywords = "Artificial Intelligence, Electronic Health Records, Survival Analysis",

author = "Kanghyuck Lee and Oh, \{Seol Whan\} and Kim, \{Sung Hwan\} and Taehoon Ko and Choi, \{In Young\}",

note = "Publisher Copyright: {\textcopyright} 2024 The Authors.; 34th Medical Informatics Europe Conference, MIE 2024 ; Conference date: 25-08-2024 Through 29-08-2024",

year = "2024",

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doi = "10.3233/SHTI240512",

language = "English",

series = "Studies in Health Technology and Informatics",

publisher = "IOS Press BV",

pages = "710--711",

editor = "John Mantas and Arie Hasman and George Demiris and Kaija Saranto and Michael Marschollek and Arvanitis, \{Theodoros N.\} and Ivana Ognjanovic and Arriel Benis and Parisis Gallos and Emmanouil Zoulias and Elisavet Andrikopoulou",

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Lee, K, Oh, SW, Kim, SH, Ko, T & Choi, IY 2024, Machine Learning-Based Predictive Models for Early Detection of Cardiovascular Diseases: A Study Utilizing Patient Samples from a Tertiary Health Promotion Center in Korea. in J Mantas, A Hasman, G Demiris, K Saranto, M Marschollek, TN Arvanitis, I Ognjanovic, A Benis, P Gallos, E Zoulias & E Andrikopoulou (eds), Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024. Studies in Health Technology and Informatics, vol. 316, IOS Press BV, pp. 710-711, 34th Medical Informatics Europe Conference, MIE 2024, Athens, Greece, 25/08/24. https://doi.org/10.3233/SHTI240512

Machine Learning-Based Predictive Models for Early Detection of Cardiovascular Diseases: A Study Utilizing Patient Samples from a Tertiary Health Promotion Center in Korea. / Lee, Kanghyuck; Oh, Seol Whan; Kim, Sung Hwan et al.
Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024. ed. / John Mantas; Arie Hasman; George Demiris; Kaija Saranto; Michael Marschollek; Theodoros N. Arvanitis; Ivana Ognjanovic; Arriel Benis; Parisis Gallos; Emmanouil Zoulias; Elisavet Andrikopoulou. IOS Press BV, 2024. p. 710-711 (Studies in Health Technology and Informatics; Vol. 316).

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

TY - GEN

T1 - Machine Learning-Based Predictive Models for Early Detection of Cardiovascular Diseases

T2 - 34th Medical Informatics Europe Conference, MIE 2024

AU - Lee, Kanghyuck

AU - Oh, Seol Whan

AU - Kim, Sung Hwan

AU - Ko, Taehoon

AU - Choi, In Young

PY - 2024/8/22

Y1 - 2024/8/22

N2 - A machine learning model was developed for cardiovascular diseases prediction based on 21,118 patient checkups data from a tertiary medical institution in Seoul, Korea, collected between 2009 and 2021. XGBoost algorithm showed the highest predictive performance, with an average AUROC of 0.877. In survival analysis, XGBSE achieved an AUROC exceeding 0.9 for 2-9 year predictions, with a C-index of 0.878 across all diseases, outperforming Cox regression (C-index of 0.887). A high-performance prediction model for cardiovascular diseases using the XGBSE algorithm was successfully developed and is poised for real-world clinical application following external simplification and validation.

AB - A machine learning model was developed for cardiovascular diseases prediction based on 21,118 patient checkups data from a tertiary medical institution in Seoul, Korea, collected between 2009 and 2021. XGBoost algorithm showed the highest predictive performance, with an average AUROC of 0.877. In survival analysis, XGBSE achieved an AUROC exceeding 0.9 for 2-9 year predictions, with a C-index of 0.878 across all diseases, outperforming Cox regression (C-index of 0.887). A high-performance prediction model for cardiovascular diseases using the XGBSE algorithm was successfully developed and is poised for real-world clinical application following external simplification and validation.

KW - Artificial Intelligence

KW - Electronic Health Records

KW - Survival Analysis

UR - https://www.scopus.com/pages/publications/85202002084

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DO - 10.3233/SHTI240512

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AN - SCOPUS:85202002084

T3 - Studies in Health Technology and Informatics

SP - 710

EP - 711

BT - Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024

A2 - Mantas, John

A2 - Hasman, Arie

A2 - Demiris, George

A2 - Saranto, Kaija

A2 - Marschollek, Michael

A2 - Arvanitis, Theodoros N.

A2 - Ognjanovic, Ivana

A2 - Benis, Arriel

A2 - Gallos, Parisis

A2 - Zoulias, Emmanouil

A2 - Andrikopoulou, Elisavet

PB - IOS Press BV

Y2 - 25 August 2024 through 29 August 2024

ER -

Lee K, Oh SW, Kim SH, Ko T, Choi IY. Machine Learning-Based Predictive Models for Early Detection of Cardiovascular Diseases: A Study Utilizing Patient Samples from a Tertiary Health Promotion Center in Korea. In Mantas J, Hasman A, Demiris G, Saranto K, Marschollek M, Arvanitis TN, Ognjanovic I, Benis A, Gallos P, Zoulias E, Andrikopoulou E, editors, Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024. IOS Press BV. 2024. p. 710-711. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI240512

Machine Learning-Based Predictive Models for Early Detection of Cardiovascular Diseases: A Study Utilizing Patient Samples from a Tertiary Health Promotion Center in Korea

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

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