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.
Highlights
Dyslipidemia is a widely recognized risk factor for cardiovascular diseases, a leading cause of death in both developed and developing countries [1,2]
The purpose of this study was to develop and deliver an artificial neural network (ANN) model without biochemical parameters to identify those at high risk of dyslipidemia in rural adult population, and evaluate the predictive performance of the ANN model in comparison with the traditional logistic regression (LR) model
Factors significantly associated with dyslipidemia were age, higher-educational level (OR = 1.362), smoking (OR = 1.165), more high-fat diet (OR = 1.403), positive family history of dyslipidemia (OR = 1.876), and central obesity (Male: Waist circumference (WC) $90 cm; Female: WC $80 cm) (OR = 2.327)
Summary
Dyslipidemia is a widely recognized risk factor for cardiovascular diseases, a leading cause of death in both developed and developing countries [1,2]. The World Health Organization (WHO) estimates that dyslipidemia is associated with more than half of the global cases of ischemic heart disease and more than four million deaths per year [3]. Estimating an individual’s risk across a range of presumed risk factors is fundamental to prevent dyslipidemia [6]. A model that integrates related factors and predicts the risk of dyslipidemia would be helpful to promote health education and counseling, and enable further development of computerized medical decision support systems for aiding healthcare practitioners to assess the risks of their patients quickly, inexpensively, and noninvasively [9,10]. Dyslipidemia is an extremely prevalent but preventable risk factor for cardiovascular disease. 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
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