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September 28, 2025خزائن للعلوم الاقتصادية والإدارية.Open Access

Using Logistic Regression and Bayesian Regression in The analysis of Chronic Disease Data

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Authors

HYHind Mohammed YousifEYEnaam Haitham Yaqoub

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Overview

Bayesian regression shows higher classification accuracy in chronic disease data compared to logistic regression, suggesting improved medical analysis.

Key Points

  • The bayesian model achieved a classification accuracy of 86.7%, significantly higher than the classical model's 63.2%.
  • The AUC value for the bayesian model was 0.91, compared to 0.87 for the classical logistic regression approach.
  • This analysis used a dataset of 50,000 public health records to evaluate chronic disease presence based on various factors.
  • The findings indicate that bayesian modeling provides a more flexible framework for handling large datasets in medical decision-making.

Cite This Study

Yousif et al. (2025) studied this question.

synapsesocial.com/papers/68d90a0641e1c178a14f6490https://doi.org/10.69938/keas.25020311
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