Abstract
BackgroundThis study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches.Methods and MaterialsWe analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared.ResultsUnivariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724–0.793) for LR and 0.762 (95% CI 0.732–0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses.ConclusionThe prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset.
Highlights
The prevalence of cardiovascular autonomic (CA) dysfunction is rapidly increasing worldwide, in developing countries
Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P,0.05)
The mean area under the receiver-operating curve was 0.758 for logistic regression (LR) and 0.762 for artificial neural network (ANN) analysis, but noninferiority result was found (P,0.001)
Summary
The prevalence of cardiovascular autonomic (CA) dysfunction is rapidly increasing worldwide, in developing countries. The disease is a major factor in the cardiovascular complications of diabetes mellitus (DM) [1], but it affects many other major segments of the general population, such as the elderly and patients with hypertension (PH), metabolic syndrome (MetS), and connective tissue disorders [2,3,4]. The prevalence of CA dysfunction in diabetic patients was found to be 30–60% [1]. This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches
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