Development and evaluation of a simple and effective prediction approach for identifying those at high risk of dyslipidemia in rural adult residents.

ABSTRACT:

Background

Dyslipidemia is an extremely prevalent but preventable risk factor for cardiovascular disease. However, many dyslipidemia patients remain undetected in resource limited settings. The study was performed to develop and evaluate a simple and effective prediction approach without biochemical parameters to identify those at high risk of dyslipidemia in rural adult population.

Methods

Demographic, dietary and lifestyle, and anthropometric data were collected by a cross-sectional survey from 8,914 participants living in rural areas aged 35-78 years. There were 6,686 participants randomly selected into a training group for constructing the artificial neural network (ANN) and logistic regression (LR) prediction models. The remaining 2,228 participants were assigned to a v

SUBMITTER: Wang CJ 

PROVIDER: S-EPMC3429495 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature