Open AccessCardiovascular Disease Prediction by Machine Learning Algorithms Based on Cytokines in Kazakhs of China
Author(s) -
Yunxing Jiang,
Xianghui Zhang,
Rulin Ma,
Xinping Wang,
Jiaming Liu,
Mulatibieke Keerman,
Yizhong Yan,
Junfeng Ma,
Yanpeng Song,
Jingyu Zhang,
Jia He,
Shuxia Guo,
Min Zhang
Publication year - 2021
Publication title -
clinical epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.868
H-Index - 58
ISSN - 1179-1349
DOI - 10.2147/clep.s313343
Subject(s) - brier score , random forest , medicine , decision tree , support vector machine , machine learning , artificial intelligence , algorithm , naive bayes classifier , gradient boosting , test set , logistic regression , receiver operating characteristic , hyperparameter , statistics , computer science , mathematics
Cardiovascular disease (CVD) is the leading cause of mortality worldwide. Accurately identifying subjects at high-risk of CVD may improve CVD outcomes. We sought to systematically examine the feasibility and performance of 7 widely used machine learning (ML) algorithms in predicting CVD risks.