ArticleClinical epidemiology2021
Cardiovascular Disease Prediction by Machine Learning Algorithms Based on Cytokines in Kazakhs of China.
Article in Clinical epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.
- Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.BMC medicine · 2024Pooled it
- Glycitin Alleviates Myocardial Ischemia-reperfusion Injury By Suppressing Oxidative Stress and Apoptosis Through PI3K/Akt/NF-κB Pathway Modulation.Applied biochemistry and biotechnology · 2026Article
- Identification and Screening of Lactate-Related Genes as Molecular Markers for Early Diagnosis of Steroid-Induced Osteonecrosis of the Femoral Head.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Article
- Machine learning approach using electrochemical immunosensor data for precise classification of Opisthorchis viverrini infection.Scientific reports · 2025Article
- Cardiovascular risk prediction via ensemble machine learning and oversampling methods.Scientific reports · 2025Article
- Evaluating the factors influencing accuracy, interpretability, and reproducibility in the use of machine learning classifiers in biology to enable standardization.Scientific reports · 2025Article
- A systematic review of machine learning in heart disease prediction.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Identification and validation of lactate-related gene signatures in endometriosis for clinical evaluation and immune characterization by WGCNA and machine learning.Frontiers in cell and developmental biology · 2025Article
- Lactate-related gene signatures as prognostic predictors and comprehensive analysis of immune profiles in nasopharyngeal carcinoma.Journal of translational medicine · 2024Article
- Comparison of the effects of imputation methods for missing data in predictive modelling of cohort study datasets.BMC medical research methodology · 2024Article
- Severity of coronary artery disease is associated with diminished circANRIL expression: A possible blood based transcriptional biomarker in East Africa.Journal of cellular and molecular medicine · 2024Article
- Advanced detection of coronary artery disease via deep learning analysis of plasma cytokine data.Frontiers in cardiovascular medicine · 2024Article
- Machine Learning Models for ASCVD Risk Prediction in an Asian Population - How to Validate the Model is Important.Acta Cardiologica Sinica · 2023Article
- Machine learning approaches for biomarker discovery to predict large-artery atherosclerosis.Scientific reports · 2023Article
- Systemic lupus erythematosus with high disease activity identification based on machine learning.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2023Article
- Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis.BMC public health · 2023Article
- Machine learning identifies prominent factors associated with cardiovascular disease: findings from two million adults in the Kashgar Prospective Cohort Study (KPCS).Global health research and policy · 2022Article
- Helicobacter pylori (H. pylori) risk factor analysis and prevalence prediction: a machine learning-based approach.BMC infectious diseases · 2022Article
- Machine learning-based risk factor analysis and prevalence prediction of intestinal parasitic infections using epidemiological survey data.PLoS neglected tropical diseases · 2022Article
- Predicting the Physician's Specialty Using a Medical Prescription Database.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
13 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCardiovascular 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.
methodsThe final analysis included 1508 Kazakh subjects in China without CVD at baseline who completed follow-up. All subjects were randomly divided into the training set (80%) and the test set (20%). L1-penalized logistic regression (LR), support vector machine with radial basis function (SVM), decision tree (DT), random forest (RF), k-nearest neighbors (KNN), Gaussian naive Bayes (NB), and extreme gradient boosting (XGB) were employed for prediction CVD outcomes. Ten-fold cross-validation was used during model developing and hyperparameters tuning in the training set. Model performance was evaluated in the test set in light of discrimination, calibration, and clinical usefulness. RF was applied to obtain the variable importance of included variables. Twenty-two variables, including sociodemographic characteristics, medical history, cytokines, and synthetic indices, were used for model development.
resultsAmong 1508 subjects, 203 were diagnosed with CVD over a median follow-up of 5.17 years. All 7 models had moderate to excellent discrimination (AUC ranged from 0.770 to 0.872) and were well calibrated. LR and SVM performed identically with an AUC of 0.872 (95% CI: 0.829-0.907) and 0.868 (95% CI: 0.825-0.904), respectively. LR had the lowest Brier score (0.078) and the highest sensitivity (97.1%). Decision curve analysis indicated that SVM was slightly better than LR. The inflammatory cytokines, such as hs-CRP and IL-6, were identified as strong predictors of CVD.
conclusionSVM and LR can be applied to guide clinical decision-making in the Kazakh Chinese population, and further study is required to ensure their accuracies.
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