基于多个特征选择和改进的PSO-XGBoost模型预测心血管疾病
Kerang Cao1,2, Chang Liu1, Siqi Yang1
1College of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang, 110142, China.
Scientific reports
|April 11, 2025
概括
这项研究引入了一种改进的心血管疾病预测模型,使用先进的特征选择和超参数优化. 新模型显示了更高的准确性和性能,有助于更好地预测和预防心脏病.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 计算生物学 计算生物学
背景情况:
- 心血管疾病对全球健康构成重大威胁.
- 准确的预测模型对于及时干预和患者管理至关重要.
- 现有的预测模型可能缺乏最佳特征选择和超参数调整.
研究的目的:
- 开发和评估一种新的心血管疾病预测模型.
- 通过集成的特征选择和超参数优化来提高预测准确性.
- 将拟议模型的性能与标准算法进行比较.
主要方法:
- 数据集预处理和初始极度梯度提升 (XGBoost) 模型构建.
- 使用皮尔森相关性和特征重要性排名进行多个特征选择.
- 使用改进的粒子优化 (PSO) 算法对XGBoost进行超参数优化,创建了MFS-DLPSO-XGBoost模型.
主要成果:
- MFS-DLPSO-XGBoost模型的回忆率为71.4%,精度为76.3%,准确度为74.7%,F1得分为73.6%,AUC为80.8%.
- 与标准XGBoost模型相比,性能指标分别显示了3.6%,3.2%,2.7%,3.2%,2.3%的改善.
- 优化的模型表现出卓越的分类性能.
结论:
- 拟议的MFS-DLPSO-XGBoost模型为心血管疾病预测提供了显著的改进.
- 多个特征选择和优化的超参数的结合提高了模型的有效性.
- 这种模型可以作为临床医生和患者在心脏病预测和预防方面的宝贵工具.
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