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Related Experiment Video

Updated: Sep 23, 2025

Three-Dimensional Printing of a Complex Aortic Anomaly
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Cluster-Based Ensemble Learning Model for Aortic Dissection Screening.

Yan Gao1, Min Wang1, Guogang Zhang2

  • 1School of Automation, Central South University, Changsha 410083, China.

International Journal of Environmental Research and Public Health
|May 14, 2022
PubMed
Summary
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This study introduces CRST-Bagging, an AI model for early aortic dissection (AD) screening. The model effectively identifies AD patients from imbalanced datasets, improving diagnostic accuracy and potentially saving lives.

Area of Science:

  • Cardiovascular Medicine
  • Artificial Intelligence
  • Machine Learning

Background:

  • Aortic dissection (AD) is a critical cardiovascular condition with high mortality rates.
  • Its varied clinical presentation often leads to delayed diagnosis or misdiagnosis.
  • Early detection is crucial for improving patient outcomes.

Purpose of the Study:

  • To develop an advanced ensemble learning model for early screening of aortic dissection (AD).
  • To address the challenge of extremely imbalanced datasets in AD diagnosis.
  • To improve the accuracy and efficiency of AD patient identification.

Main Methods:

  • Proposed a novel ensemble learning model: Cluster Random under-sampling Smote-Tomek Bagging (CRST-Bagging).
  • Introduced the CRST method, integrating Kmeans++ and Smote-Tomek for effective data resampling.
Keywords:
aortic dissectionbaggingclusteringimbalanced datascreening

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  • Utilized Bagging algorithm for AD patient prediction on routine examination data.

    • Main Results:

    • The CRST method demonstrated effectiveness in resampling imbalanced AD datasets.
    • CRST-Bagging outperformed RUSBoost and SMOTEBagging in AD patient screening.
    • Achieved 83.6% precision, 80.7% recall, and an 82.1% F1-score on the test dataset.

    Conclusions:

    • The CRST-Bagging model shows significant effectiveness in screening aortic dissection patients.
    • The proposed model offers a promising tool for early AD detection, potentially reducing mortality.
    • This approach addresses data imbalance issues in cardiovascular disease prediction.