ArticlePloS one2012
Development and evaluation of a simple and effective prediction approach for identifying those at high risk of dyslipidemia in rural adult residents.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 44 citations in OpenAlex.
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- Novel anthropometric indices for predicting type 2 diabetes mellitus.BMC public health · 2024Article
- Prediction of hypercholesterolemia using machine learning techniques.Journal of diabetes and metabolic disorders · 2023Article
- Unconventional Cardiovascular Risk Factors and Systematic Coronary Risk Estimation (SCORE) in the Lebanese Rural Population: The Forgotten Factors.Vascular health and risk management · 2023Article
- Genetic factors increase the identification efficiency of predictive models for dyslipidaemia: a prospective cohort study.Lipids in health and disease · 2021Article
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- Modeling the risk factors for dyslipidemia and blood lipid indices: Ravansar cohort study.Lipids in health and disease · 2020Article
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- Develop and Evaluate a New and Effective Approach for Predicting Dyslipidemia in Steel Workers.Frontiers in bioengineering and biotechnology · 2020Article
- Application of a decision tree to establish factors associated with a nomogram of aortic stiffness.Journal of clinical hypertension (Greenwich, Conn.) · 2019Article
- Metabolic-related markers and inflammatory factors as predictors of dyslipidemia among urban Han Chinese adults.Lipids in health and disease · 2019Article
- Associations between lipid profiles of adolescents and their mothers based on a nationwide health and nutrition survey in South Korea.BMJ open · 2019Article
- Application of Latent Class Analysis to Identify Metabolic Syndrome Components Patterns in adults: Tehran Lipid and Glucose study.Scientific reports · 2019Article
- Risk Prediction of Dyslipidemia for Chinese Han Adults Using Random Forest Survival Model.Clinical epidemiology · 2019Article
- Comparative Study of Back Propagation Artificial Neural Networks and Logistic Regression Model in Predicting Poor Prognosis after Acute Ischemic Stroke.Open medicine (Warsaw, Poland) · 2019Article
- Risk prediction model of dyslipidaemia over a 5-year period based on the Taiwan MJ health check-up longitudinal database.Lipids in health and disease · 2018Article
- Identification of an obesity index for predicting metabolic syndrome by gender: the rural Chinese cohort study.BMC endocrine disorders · 2018Article
- A Comparison between Decision Tree and Random Forest in Determining the Risk Factors Associated with Type 2 Diabetes.Journal of research in health sciences · 2018Article
- Prediction of dyslipidemia using gene mutations, family history of diseases and anthropometric indicators in children and adolescents: The CASPIAN-III study.Computational and structural biotechnology journal · 2018Article
- Risk factors and quality of life of dyslipidemic patients in Lebanon: A cross-sectional study.Journal of epidemiology and global health · 2016Observational
Corrections and comments
- Retraction · 2021-04-16Copyright Claims · Lack of Approval from Third Party ·
- Retracted
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDyslipidemia 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.
methodsDemographic, 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 validation group for performance comparisons of ANN and LR models. The predictors of dyslipidemia risk were identified from the training group using multivariate logistic regression analysis. Predictive performance was evaluated by receiver operating characteristic (ROC) curve.
resultsSome risk factors were significantly associated with dyslipidemia, including age, gender, educational level, smoking, high-fat diet, vegetable and fruit intake, family history, physical activity, and central obesity. For the ANN model, the sensitivity, specificity, positive and negative likelihood ratio, positive and negative predictive values were 90.41%, 76.66%, 3.87, 0.13, 76.33%, and 90.58%, respectively, while LR model were only 57.37%, 70.91%, 1.97, 0.60, 62.09%, and 66.73%, respectively. The area under the ROC cure (AUC) value of the ANN model was 0.86±0.01, showing more accurate overall performance than traditional LR model (AUC = 0.68±0.01, P<0.001).
conclusionThe ANN model is a simple and effective prediction approach to identify those at high risk of dyslipidemia, and it can be used to screen undiagnosed dyslipidemia patients in rural adult population. Further work is planned to confirm these results by incorporating multi-center and longer follow-up data.
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