Research on Stroke Risk Prediction for Imbalanced Datasets

YU Cheng-long; SUN Yue; GUO Jin-xing; CAI Ying; CHEN Guang-xin; FENG Ying

doi:10.3969/j.issn.2096-6091.2024.03.004

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Research on Stroke Risk Prediction for Imbalanced Datasets

RESEARCH PAPER | 更新时间：2025-04-08

- Research on Stroke Risk Prediction for Imbalanced Datasets
- New Generation of Information Technology Vol. 7, Issue 3, Pages: 18-22(2024)
- 作者机构：
  
  1.牡丹江医学院附属红旗医院，黑龙江牡丹江 157011
  2.牡丹江医学院，黑龙江牡丹江 157011
  3.牡丹江医学院附属第二医院，黑龙江牡丹江 157011
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.2096-6091.2024.03.004
  CLC： TP181
- Published：25 March 2024
- 稿件说明：
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于成龙, 孙悦, 郭金兴, 等. 面向不平衡数据集的脑卒中风险预测研究[J]. 新一代信息技术, 2024, 7(3): 18-22

YU Cheng-long, SUN Yue, GUO Jin-xing, et al. Research on Stroke Risk Prediction for Imbalanced Datasets[J]. New Generation of Information Technology, 2024, 7(3): 18-22
于成龙, 孙悦, 郭金兴, 等. 面向不平衡数据集的脑卒中风险预测研究[J]. 新一代信息技术, 2024, 7(3): 18-22 DOI： 10.3969/j.issn.2096-6091.2024.03.004.

YU Cheng-long, SUN Yue, GUO Jin-xing, et al. Research on Stroke Risk Prediction for Imbalanced Datasets[J]. New Generation of Information Technology, 2024, 7(3): 18-22 DOI： 10.3969/j.issn.2096-6091.2024.03.004.

摘要

为解决脑卒中风险预测中数据不平衡的问题，本文旨在通过数据扩充方法提高模型对少数类的识别能力。采用过采样技术（Synthetic Minority Over-sampling TEchnique，SMOTE）对训练数据进行扩充，并使用逻辑回归、支持向量机、决策树、

近邻、随机森林、梯度提升机、XGBoost等多种机器学习模型进行实验。通过比较在原始不平衡数据集和经过过采样技术处理的数据集上的模型性能，评估了SMOTE对模型预测能力的影响。实验结果显示，在原始不平衡数据集上，模型普遍难以识别少数类。而在经过SMOTE处理的数据集上，各模型的准确率、G-mean和

值等性能指标均显著提高。特别是基于树的模型和集成方法在处理不平衡数据时表现出更高的有效性。

Abstract

In order to solve the problem of data imbalance in stroke risk prediction

the purpose of this study is to improve the model's ability to identify minority classes by data expansion method. SMOTE is used to expand the training data

and the experiment is carried out with logistic regression

support vector machine

decision tree

-nearest neighbor

random forest

gradient elevator

XGBoost and other machine learning models. The effect of SMOTE technique on the predictive power of the model is evaluated by comparing the performance of the model on the original unbalanced data set and the SMOTE treated data set.The experimental results show that on the original unbalanced data set

the model is generally difficult to identify a few classes. In the dataset treated by SMOTE

the accuracy

G-mean and

values of each model have improved significantly. In particular

tree-based models and integration methods show h

igher effectiveness in dealing with unbalanced data.

关键词

Keywords

references

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