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<p>Aortic Aneurysm (A<sup>2</sup>) remains a leading cause of morbidity and mortality worldwide, necessitating innovative approaches for accurate prediction and early intervention. This study proposes a novel ensemble learning framework that integrates bagging and boosting techniques through stacking, combined with Chi-square feature selection, to enhance the prediction of A<sup>2</sup> condition using biomarker profiling. The stacking method leverages the strengths of individual models, including Decision Trees (DT), Random Forests (RF), and AdaBoost, GBoost, and XGBoost, to create a robust meta-model. The experimental evaluations are then conducted on Aorta vessel Tree (AVT) dataset taken from public repository. Chi-square (Chi<sup>2</sup>) feature selection is utilized to identify the most significant biomarkers, reducing dimensionality and ensuring that only the most relevant features contribute to the model. This preprocessing step enhances the model’s interpretability and performance by focusing on the critical factors associated with Aortic Aneurysm. The proposed framework is evaluated using key performance metrics, demonstrating superior predictive performance compared to conventional individual-model approaches.</p>

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SBB-Chi2-A2: stacking of bagging-boosting with the blend Chi-square for effective prediction of aortic aneurysm using biomarker profiling

  • 期刊论文

  • 发表日期：

    2025年1月12日

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  • Sanjuktarani Jena,
  • Biswajit Brahma,
  • Zabiha Khan,
  • G. Jyothi,
  • P. Arunachalam,
  • Saurabh Aggarwal

摘要

Aortic Aneurysm (A2) remains a leading cause of morbidity and mortality worldwide, necessitating innovative approaches for accurate prediction and early intervention. This study proposes a novel ensemble learning framework that integrates bagging and boosting techniques through stacking, combined with Chi-square feature selection, to enhance the prediction of A2 condition using biomarker profiling. The stacking method leverages the strengths of individual models, including Decision Trees (DT), Random Forests (RF), and AdaBoost, GBoost, and XGBoost, to create a robust meta-model. The experimental evaluations are then conducted on Aorta vessel Tree (AVT) dataset taken from public repository. Chi-square (Chi2) feature selection is utilized to identify the most significant biomarkers, reducing dimensionality and ensuring that only the most relevant features contribute to the model. This preprocessing step enhances the model’s interpretability and performance by focusing on the critical factors associated with Aortic Aneurysm. The proposed framework is evaluated using key performance metrics, demonstrating superior predictive performance compared to conventional individual-model approaches.