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The association between liver disease and stroke risk: A cross-sectional study with machine learning in a large-scale

Junchen Chen1, Yashi Chen1, Shunqiu Huang1

  • 1Department of Neurosurgery, The First Affiliated Hospital of Shantou University Medical College, No.57 Changping Road, Shantou, Guangdong, 515041, China.

International Journal of Cardiology. Cardiovascular Risk and Prevention
|February 26, 2026
PubMed

Insights

Liver disease is significantly linked to stroke in Chinese adults over 45. This study highlights the importance of rigorous methods when analyzing health data, especially for conditions like liver disease and stroke.

Area of Science:

  • Public Health
  • Epidemiology
  • Gerontology

Background:

  • Liver disease (LD) is a growing global health concern.
  • Stroke remains a leading cause of disability and mortality worldwide.
  • Understanding the relationship between chronic conditions like LD and cerebrovascular events is crucial for preventative strategies.

Purpose of the Study:

  • To investigate the association between liver disease and stroke in Chinese adults aged 45 and older.
  • To utilize cross-sectional data from the China Health and Retirement Longitudinal Study (CHARLS).
  • To explore the utility of machine learning models in assessing this association.

Main Methods:

  • Cross-sectional analysis of 4586 participants from the 2018 CHARLS wave.
  • Sequential multivariable logistic regression and weighted stratified analyses were employed.
  • Machine learning models (SVM, LR, RPART, RF, NB) were explored, with data preprocessed using Random Over-Sampling Examples (ROSE) to manage class imbalance.

Main Results:

  • A significant association was found between liver disease and stroke (P < 0.001).
  • In the fully adjusted model, LD remained significantly associated with stroke (OR = 2.6, P = 0.001).
  • Machine learning models did not show meaningful predictive performance for stroke in this dataset after adjustments.

Conclusions:

  • This study confirms a significant association between liver disease and stroke in older Chinese adults.
  • Methodological rigor, particularly in handling class imbalance, is critical for accurate analysis.
  • The findings suggest a concurrent link that warrants further longitudinal research to explore causality.
Abstract

Keywords:
China health and retirement longitudinal study (CHARLS)Cross-sectional studyLiver diseaseMachine learningReceiver operating characteristicStroke

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