基于K-Means BS和TW-gcForest的不足样本大小对动脉样硬化风险的评估
Yudong Zhang1, Wenjun Liu1, Lidan He1
1School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, China.
Computer methods in biomechanics and biomedical engineering
|March 20, 2025
概括
数据不足阻碍了动脉样硬化风险评估. 新的方法,K-Means Borderline-SMOTE和三重加权的gcForest,提高了分类准确性,以便更好地评估患者的风险.
科学领域:
- 心血管疾病研究研究
- 医疗保健中的机器学习
- 数据科学用于风险预测和风险预测.
背景情况:
- 数据不足是动脉样硬化风险评估研究的一个重大挑战.
- 有限的样本大小往往导致标准分类算法的性能不足.
- 这限制了动脉样硬化数据对患者风险分层的有效应用.
研究的目的:
- 开发一种有效的算法模型,用于评估不充分样本大小的患者的动脉样硬化风险.
- 在医学研究中处理小数据集时克服传统分类方法的局限性.
主要方法:
- 提出了一个过量采样技术,K-Means-Borderline-SMOTE (K-Means BS),以产生具有严格约束的多样化合成样本.
- 引入了一种新的分类算法,即三重加权的gcForest (TW-gcForest),以解决标准gcForest中的重量分配问题.
- 在两个数据集上通过数值模拟证明了K-Means BS和TW-gcForest的稳定性.
主要成果:
- 标准gcForest在K-Means BS合成数据集上表现良好,用于动脉样硬化风险评估.
- 在原始,SMOTE和K-Means BS合成数据集上,TW-gcForest显著超过了标准的gcForest.
- 数字模拟证实了拟议方法的有效性.
结论:
- 开发的K-Means BS和TW-gcForest方法提高了准确性,精度,回忆,F1得分和AUC.
- 这种方法为动脉样硬化风险评估提供了可靠的解决方案,即使数据有限.
- 这些发现表明,在应用机器学习来预测心血管风险方面,有前途的进展.
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