Risk Prediction of Dyslipidemia for Chinese Han Adults Using Random Forest Survival Model
Authors Zhang X, Tang F, Ji J, Han W, Lu P
Received 18 July 2019
Accepted for publication 29 November 2019
Published 10 December 2019 Volume 2019:11 Pages 1047—1055
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 2
Editor who approved publication: Professor Eyal Cohen
Xiaoshuai Zhang,1 Fang Tang,2 Jiadong Ji,1 Wenting Han,3 Peng Lu3
1School of Statistics, Shandong University of Finance and Economics, Jinan, People’s Republic of China; 2Center for Data Science in Health and Medicine, Shandong Provincial Qianfoshan Hospital, The First Hospital Affiliated with Shandong First Medical University, Jinan, People’s Republic of China; 3Department of Preventive Medicine, School of Public Health and Management, Binzhou Medical University, Yantai, People’s Republic of China
Correspondence: Xiaoshuai Zhang
School of Statistics, Shandong University of Finance and Economics, Jinan 250014, People’s Republic of China
Tel +86 13589896463
Objective: Dyslipidemia 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.
Methods: We 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.
Results: The 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.
Conclusion: The RSF model is a promising method in identifying high-risk individuals for the prevention of dyslipidemia and related diseases.
Keywords: random survival forest, Cox proportional hazard model, dyslipidemia, risk prediction
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