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Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis

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  • Zi-Hui Tang
  • Juanmei Liu
  • Fangfang Zeng
  • Zhongtao Li
  • Xiaoling Yu
  • Linuo Zhou

Abstract

Background: 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. Methods and Materials: We 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. Results: Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P

Suggested Citation

  • Zi-Hui Tang & Juanmei Liu & Fangfang Zeng & Zhongtao Li & Xiaoling Yu & Linuo Zhou, 2013. "Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
    • Handle: RePEc:plo:pone00:0070571
    DOI: 10.1371/journal.pone.0070571
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    1. Po-Hsiang Lin & Jer-Guang Hsieh & Hsien-Chung Yu & Jyh-Horng Jeng & Chiao-Lin Hsu & Chien-Hua Chen & Pin-Chieh Wu, 2021. "Risk Prediction of Barrett’s Esophagus in a Taiwanese Health Examination Center Based on Regression Models," IJERPH, MDPI, vol. 18(10), pages 1-10, May.

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