Artificial neural network models for prediction of cardiovascular autonomic dysfunction in general Chinese population

Juanmei Liu,Zi-Hui Tang,Fangfang Zeng,Linuo Zhou,Zhongtao Li

doi:10.1186/1472-6947-13-80

Juanmei Liu, Zi-Hui Tang + Show 3 more

Open Access

https://doi.org/10.1186/1472-6947-13-80

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Abstract

BackgroundThe present study aimed to develop an artificial neural network (ANN) based prediction model for cardiovascular autonomic (CA) dysfunction in the general population.MethodsWe analyzed a previous dataset based on a population sample consisted of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN analysis. Performances of these prediction models were evaluated in the validation set.ResultsUnivariate analysis indicated that 14 risk factors showed statistically significant association with CA dysfunction (P < 0.05). The mean area under the receiver-operating curve was 0.762 (95% CI 0.732–0.793) for prediction model developed using ANN analysis. The mean sensitivity, specificity, positive and negative predictive values were similar in the prediction models was 0.751, 0.665, 0.330 and 0.924, respectively. All HL statistics were less than 15.0.ConclusionANN is an effective tool for developing prediction models with high value for predicting CA dysfunction among the general population.

Highlights

The present study aimed to develop an artificial neural network (ANN) based prediction model for cardiovascular autonomic (CA) dysfunction in the general population
A prediction model refers to the type of medical research study using which researchers try to identify the best combination of medical signs, symptoms, and other findings that may be used to predict the probability of a specific disease or outcome [6]
ANN models have been successfully used to predict trauma mortality and in clinical decision-making in the management of traumatic brain injury patients [10,11]

Summary

Introduction

The present study aimed to develop an artificial neural network (ANN) based prediction model for cardiovascular autonomic (CA) dysfunction in the general population. A prediction model refers to the type of medical research study using which researchers try to identify the best combination of medical signs, symptoms, and other findings that may be used to predict the probability of a specific disease or outcome [6]. These models may aid the clinician in the decision-making process regarding clinical admission, early prevention, early clinical diagnosis, and application of clinical therapies. The aim of this study was to develop a prediction model for CA dysfunction using ANN analysis

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