Genetic factors increase the identification efficiency of predictive models for dyslipidaemia: a prospective cohort study.

ABSTRACT:

Background

Few studies have developed risk models for dyslipidaemia, especially for rural populations. Furthermore, the performance of genetic factors in predicting dyslipidaemia has not been explored. The purpose of this study is to develop and evaluate prediction models with and without genetic factors for dyslipidaemia in rural populations.

Methods

A total of 3596 individuals from the Henan Rural Cohort Study were included in this study. According to the ratio of 7:3, all individuals were divided into a training set and a testing set. The conventional models and conventional+GRS (genetic risk score) models were developed with Cox regression, artificial neural network (ANN), random forest (RF), and gradient boosting machine (GBM) classifiers in the training set. The area u

SUBMITTER: Niu M 

PROVIDER: S-EPMC7881493 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature