Accuracy of Machine Learning in Predicting Post-Stroke Depression: A Systematic Review and Meta-Analysis.

Journal: Brain and behavior
Published Date: May 1, 2025

Abstract

INTRODUCTION: Post-stroke depression is one of the important complications of stroke and affects patients' quality of life. Early identification of post-stroke depression is crucial for its timely prevention. The accuracy of machine learning as a prediction method is controversial. To systematically analyze these studies, we conducted a systematic evaluation to review the effectiveness of the machine learning prediction models in predicting post-stroke depression based on meta-analysis.

Authors

  • Husile Husile
    Inner Mongolia Medical University, Hohhot Inner Mongolia, China.
  • Qinglin Bao
    International Mongolian Hospital of Inner Mongolia, Hohhot Inner Mongolia, China.
  • Sarula Sarula
    International Mongolian Hospital of Inner Mongolia, Hohhot Inner Mongolia, China.
  • Chu La
    College of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
  • Wujisiguleng Wujisiguleng
    International Mongolian Hospital of Inner Mongolia, Hohhot Inner Mongolia, China.
  • Siqintu Siqintu
    Inner Mongolia Medical University, Hohhot Inner Mongolia, China.
  • Temuqile Temuqile
    International Mongolian Hospital of Inner Mongolia, Hohhot Inner Mongolia, China.

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