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

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

Abstract

Background: The present study aimed to develop an artificial neural network (ANN) based prediction model for cardiovascular autonomic (CA) dysfunction in the general population.

Methods: We 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.

Results: Univariate 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.

Conclusion: ANN is an effective tool for developing prediction models with high value for predicting CA dysfunction among the general population.

Figures

Figure 1
Figure 1
Artificial network model showing input variables (nodes), hidden nodes, and connection weights with output node for data on CA dysfunction. The ANN model including 14 input nodes, 18 hidden nodes and 1 output node. BMI- Body mass index, WC-waist circumference, SBP- systolic blood pressure, DBP- diastolic blood pressure, FPG- fasting plasma glucose, PBG- plasma blood glucose, IR-insulin resistance, TG- triglyceride, UA- uric acid, HR-heart rate, PH- Hypertension, DM- Diabetes, PHD- Hypertension duration, DMD- Diabetes duration.

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Source: PubMed

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