Model for Predicting Intracranial Aneurysm Rupture Risk Based on Ant Colony Optimization and Support Vector Machine

CHEN Guang-xin; GUO Wei; CAI Ying; GUO Jin-xing

doi:10.12263/newIT.2024.06.001

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Model for Predicting Intracranial Aneurysm Rupture Risk Based on Ant Colony Optimization and Support Vector Machine

RESEARCH PAPER | 更新时间：2025-07-04

- Model for Predicting Intracranial Aneurysm Rupture Risk Based on Ant Colony Optimization and Support Vector Machine
- New Generation of Information Technology Vol. 8, Issue 1, Pages: 1-5(2025)
- 作者机构：
  
  1.牡丹江医科大学，黑龙江牡丹江 157011
  2.牡丹江医科大学附属红旗医院，黑龙江牡丹江 157011
- 作者简介：
- 基金信息：
- DOI：10.12263/newIT.2024.06.001
  CLC： TP181
- Published：15 January 2025
- 稿件说明：
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陈广新, 国威, 才莹, 等. 基于蚁群优化支持向量机的颅内动脉瘤破裂风险预测模型[J]. 新一代信息技术, 2025, 8(1): 01-05

CHEN Guang-xin, GUO Wei, CAI Ying, et al. Model for Predicting Intracranial Aneurysm Rupture Risk Based on Ant Colony Optimization and Support Vector Machine[J]. New Generation of Information Technology, 2025, 8(1): 01-05
陈广新, 国威, 才莹, 等. 基于蚁群优化支持向量机的颅内动脉瘤破裂风险预测模型[J]. 新一代信息技术, 2025, 8(1): 01-05 DOI： 10.12263/newIT.2024.06.001.

CHEN Guang-xin, GUO Wei, CAI Ying, et al. Model for Predicting Intracranial Aneurysm Rupture Risk Based on Ant Colony Optimization and Support Vector Machine[J]. New Generation of Information Technology, 2025, 8(1): 01-05 DOI： 10.12263/newIT.2024.06.001.

摘要

本文提出了一种结合蚁群优化算法（Ant Colony Optimization，ACO）与支持向量机（Support Vector Machine，SVM）的分类模型，用于颅内动脉瘤破裂风险的预测。数据集的构建充分考虑了动脉瘤的形态学和血流动力学特征，通过CT图像的处理和计算，构建了一个全面的数据集。ACO算法用于优化SVM的参数选择，提高了模型的预测性能。支持向量机中选择了四种不同的核函数，通过混淆矩阵和ROC曲线评估了不同核函数模型的性能。结果表明，Sigmoid核函数模型的性能最佳，为颅内动脉瘤的诊断和风险评估提供了重要的参考依据。

Abstract

This paper proposes a classification model that combines the ant colony optimization (ACO) algorithm with the support vector machine (SVM) for the prediction of intracranial aneurysm rupture risk. The dataset construction takes into full account the morphological and hemodynamic characteristics of aneurysms. Through the processing and calculation of CT images

a comprehensive dataset has been constructed. The ACO algorithm is used to optimize the parameter selection of SVM

which improves the predictive performance of the model. Four different kernel functions are selected in the SVM

and the performance of the models with different kernel functions is evaluated using confusion matrices and ROC curves. The results indicate that the Sigmoid kernel function model performs the best

providing an important reference for the diagnosis and risk assessment of intracranial aneurysms.

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references

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ISHIHARA M , SHIIBA M , MARUNO H , et al . Detection of intracranial aneurysms using deep learning-based CAD system: Usefulness of the scores of CNN's final layer for distinguishing between aneurysm and infundibular dilatation [J ] . Japanese Journal of Radiology , 2023 , 41 ( 2 ): 131 - 141 .

JOO B , AHN S S , YOON P H , et al . A deep learning algorithm may automate intracranial aneurysm detection on MR angiography with high diagnostic performance [J ] . European Radiology , 2020 , 30 ( 11 ): 5785 - 5793 .

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