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Related Experiment Video

Updated: Jun 18, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Comparison of Predictive Models for Severe Dengue: Logistic Regression, Classification Tree, and the Structural

Hyelan Lee1,2, Anon Srikiatkhachorn3,4, Siripen Kalayanarooj5

  • 1Graduate School of Urban Public Health, University of Seoul, Republic of Korea.

The Journal of Infectious Diseases
|July 30, 2024
PubMed
Summary

Structural Equation Models (SEM) show comparable predictive performance to logistic regression and classification trees for identifying severe dengue illness. This finding aids in early detection and management of severe dengue cases.

Keywords:
classification treelogistic regressionpredictive modelpredictive validitysevere denguestructural equation model

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Infectious Diseases

Background:

  • Predicting severe dengue illness is crucial for timely intervention.
  • Existing models often rely on logistic regression or classification trees.
  • Comparing the performance of different statistical models is essential for improving diagnostic accuracy.

Purpose of the Study:

  • To compare the predictive performance of logistic regression, classification tree, and Structural Equation Models (SEM) for severe dengue illness.
  • To evaluate the effectiveness of these models using demographic and laboratory data.
  • To identify the most accurate model for predicting severe dengue.

Main Methods:

  • Utilized a modified dengue severity classification based on WHO 1997 guidelines.
  • Developed predictive models using demographic and laboratory indicators from Thai pediatric cohorts.
  • Employed logistic regression, classification tree, and SEM for model development.
  • Performed external validation using independent patient datasets.

Main Results:

  • Structural Equation Models (SEM) demonstrated strong predictive performance (AUC 0.73-0.85).
  • Logistic regression models also showed good discrimination (AUC 0.65-0.84).
  • Classification trees exhibited high sensitivity (0.95-0.99) but low specificity (0.10-0.44).

Conclusions:

  • Structural Equation Models (SEM) are a viable and comparable alternative to traditional methods for predicting severe dengue.
  • The findings suggest SEM can be effectively used alongside logistic regression and classification trees.
  • This research contributes to better predictive tools for severe dengue illness management.