Pre-existing and machine learning-based models for cardiovascular risk prediction

Abstract Predicting the risk of cardiovascular disease is the key to primary prevention. Machine learning has attracted attention in analyzing increasingly large, complex healthcare data. We assessed discrimination and calibration of pre-existing cardiovascular risk prediction models and developed m...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Sang-Yeong Cho, Sun-Hwa Kim, Si-Hyuck Kang, Kyong Joon Lee, Dongjun Choi, Seungjin Kang, Sang Jun Park, Tackeun Kim, Chang-Hwan Yoon, Tae-Jin Youn, In-Ho Chae
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/68aaa9b55dd04e6aa28fd62a172d6c4e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:68aaa9b55dd04e6aa28fd62a172d6c4e
record_format	dspace
spelling	oai:doaj.org-article:68aaa9b55dd04e6aa28fd62a172d6c4e2021-12-02T16:56:02ZPre-existing and machine learning-based models for cardiovascular risk prediction10.1038/s41598-021-88257-w2045-2322https://doaj.org/article/68aaa9b55dd04e6aa28fd62a172d6c4e2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88257-whttps://doaj.org/toc/2045-2322Abstract Predicting the risk of cardiovascular disease is the key to primary prevention. Machine learning has attracted attention in analyzing increasingly large, complex healthcare data. We assessed discrimination and calibration of pre-existing cardiovascular risk prediction models and developed machine learning-based prediction algorithms. This study included 222,998 Korean adults aged 40–79 years, naïve to lipid-lowering therapy, had no history of cardiovascular disease. Pre-existing models showed moderate to good discrimination in predicting future cardiovascular events (C-statistics 0.70–0.80). Pooled cohort equation (PCE) specifically showed C-statistics of 0.738. Among other machine learning models such as logistic regression, treebag, random forest, and adaboost, the neural network model showed the greatest C-statistic (0.751), which was significantly higher than that for PCE. It also showed improved agreement between the predicted risk and observed outcomes (Hosmer–Lemeshow χ2 = 86.1, P < 0.001) than PCE for whites did (Hosmer–Lemeshow χ2 = 171.1, P < 0.001). Similar improvements were observed for Framingham risk score, systematic coronary risk evaluation, and QRISK3. This study demonstrated that machine learning-based algorithms could improve performance in cardiovascular risk prediction over contemporary cardiovascular risk models in statin-naïve healthy Korean adults without cardiovascular disease. The model can be easily adopted for risk assessment and clinical decision making.Sang-Yeong ChoSun-Hwa KimSi-Hyuck KangKyong Joon LeeDongjun ChoiSeungjin KangSang Jun ParkTackeun KimChang-Hwan YoonTae-Jin YounIn-Ho ChaeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sang-Yeong Cho Sun-Hwa Kim Si-Hyuck Kang Kyong Joon Lee Dongjun Choi Seungjin Kang Sang Jun Park Tackeun Kim Chang-Hwan Yoon Tae-Jin Youn In-Ho Chae Pre-existing and machine learning-based models for cardiovascular risk prediction
description	Abstract Predicting the risk of cardiovascular disease is the key to primary prevention. Machine learning has attracted attention in analyzing increasingly large, complex healthcare data. We assessed discrimination and calibration of pre-existing cardiovascular risk prediction models and developed machine learning-based prediction algorithms. This study included 222,998 Korean adults aged 40–79 years, naïve to lipid-lowering therapy, had no history of cardiovascular disease. Pre-existing models showed moderate to good discrimination in predicting future cardiovascular events (C-statistics 0.70–0.80). Pooled cohort equation (PCE) specifically showed C-statistics of 0.738. Among other machine learning models such as logistic regression, treebag, random forest, and adaboost, the neural network model showed the greatest C-statistic (0.751), which was significantly higher than that for PCE. It also showed improved agreement between the predicted risk and observed outcomes (Hosmer–Lemeshow χ2 = 86.1, P < 0.001) than PCE for whites did (Hosmer–Lemeshow χ2 = 171.1, P < 0.001). Similar improvements were observed for Framingham risk score, systematic coronary risk evaluation, and QRISK3. This study demonstrated that machine learning-based algorithms could improve performance in cardiovascular risk prediction over contemporary cardiovascular risk models in statin-naïve healthy Korean adults without cardiovascular disease. The model can be easily adopted for risk assessment and clinical decision making.
format	article
author	Sang-Yeong Cho Sun-Hwa Kim Si-Hyuck Kang Kyong Joon Lee Dongjun Choi Seungjin Kang Sang Jun Park Tackeun Kim Chang-Hwan Yoon Tae-Jin Youn In-Ho Chae
author_facet	Sang-Yeong Cho Sun-Hwa Kim Si-Hyuck Kang Kyong Joon Lee Dongjun Choi Seungjin Kang Sang Jun Park Tackeun Kim Chang-Hwan Yoon Tae-Jin Youn In-Ho Chae
author_sort	Sang-Yeong Cho
title	Pre-existing and machine learning-based models for cardiovascular risk prediction
title_short	Pre-existing and machine learning-based models for cardiovascular risk prediction
title_full	Pre-existing and machine learning-based models for cardiovascular risk prediction
title_fullStr	Pre-existing and machine learning-based models for cardiovascular risk prediction
title_full_unstemmed	Pre-existing and machine learning-based models for cardiovascular risk prediction
title_sort	pre-existing and machine learning-based models for cardiovascular risk prediction
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/68aaa9b55dd04e6aa28fd62a172d6c4e
work_keys_str_mv	AT sangyeongcho preexistingandmachinelearningbasedmodelsforcardiovascularriskprediction AT sunhwakim preexistingandmachinelearningbasedmodelsforcardiovascularriskprediction AT sihyuckkang preexistingandmachinelearningbasedmodelsforcardiovascularriskprediction AT kyongjoonlee preexistingandmachinelearningbasedmodelsforcardiovascularriskprediction AT dongjunchoi preexistingandmachinelearningbasedmodelsforcardiovascularriskprediction AT seungjinkang preexistingandmachinelearningbasedmodelsforcardiovascularriskprediction AT sangjunpark preexistingandmachinelearningbasedmodelsforcardiovascularriskprediction AT tackeunkim preexistingandmachinelearningbasedmodelsforcardiovascularriskprediction AT changhwanyoon preexistingandmachinelearningbasedmodelsforcardiovascularriskprediction AT taejinyoun preexistingandmachinelearningbasedmodelsforcardiovascularriskprediction AT inhochae preexistingandmachinelearningbasedmodelsforcardiovascularriskprediction
_version_	1718382803996901376

Pre-existing and machine learning-based models for cardiovascular risk prediction

Ejemplares similares