ArticleClinical epidemiology2019
Risk Prediction of Dyslipidemia for Chinese Han Adults Using Random Forest Survival Model.
Article in Clinical epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 33 citations in OpenAlex.
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- Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis.BMC public health · 2023Article
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- Development and visualization of a risk prediction model for metabolic syndrome: a longitudinal cohort study based on health check-up data in China.Frontiers in nutrition · 2023Article
- Identifying the long-term survival beneficiary of chemotherapy for stage N1c sigmoid colon cancer.Scientific reports · 2022Article
- Predicting COVID-19 community infection relative risk with a Dynamic Bayesian Network.Frontiers in public health · 2022Article
- Development and validation of a risk index to predict kidney graft survival: the kidney transplant risk index.BMC medical research methodology · 2021Article
- Genetic factors increase the identification efficiency of predictive models for dyslipidaemia: a prospective cohort study.Lipids in health and disease · 2021Article
- Develop and Evaluate a New and Effective Approach for Predicting Dyslipidemia in Steel Workers.Frontiers in bioengineering and biotechnology · 2020Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveDyslipidemia has been recognized as a major risk factor of several diseases, and early prevention and management of dyslipidemia is effective in the primary prevention of cardiovascular events. The present study aims to develop risk models for predicting dyslipidemia using Random Survival Forest (RSF), which take the complex relationship between the variables into account.
methodsWe used data from 6328 participants aged between 19 and 90 years free of dyslipidemia at baseline with a maximum follow-up of 5 years. RSF was applied to develop gender-specific risk model for predicting dyslipidemia using variables from anthropometric and laboratory test in the cohort. Cox regression was also adopted in comparison with the RSF model, and Harrell's concordance statistic with 10-fold cross-validation was used to validate the models.
resultsThe incidence density of dyslipidemia was 101/1000 in total and subgroup incidence densities were 121/1000 for men and 69/1000 for women. Twenty-four predictors were identified in the prediction model of males and 23 in females. The C-statistics of the prediction models for males and females were 0.731 and 0.801, respectively. The RSF model shows better discriminative performance than CPH model (0.719 for males and 0.787 for females). Moreover, some predictors were observed to have a nonlinear effect on dyslipidemia.
conclusionThe RSF model is a promising method in identifying high-risk individuals for the prevention of dyslipidemia and related diseases.
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