基于粒子群优化-支持向量机的高血压风险预测模型的开发和验证
Rou You1, Qiaoli Tao1, Siqi Wang1
1School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510006, China.
Bioengineering (Basel, Switzerland)
|March 28, 2025
概括
粒子集群优化支持矢量机 (PSO-SVM) 显著改善了老年人早期高血压检测. 这种机器学习方法为诊断高血压提供了更高的准确性和灵敏性.
科学领域:
- 老年学是一门学科.
- 计算医学是一种计算医学.
- 生物医学工程 生物医学工程
背景情况:
- 高血压是一种普遍的健康问题,特别是在老年人群中,导致各种并发症.
- 准确及时检测高血压对于有效的患者管理至关重要.
- 传统的诊断方法可能缺乏精度和效率,需要先进的计算方法.
研究的目的:
- 评估机器学习模型的有效性,特别是支持矢量机 (SVM) 和粒子集群优化支持矢量机 (PSO-SVM),用于对老年人增强高血压检测.
- 为了比较PSO-SVM与标准SVM在识别高血压方面的性能.
主要方法:
- 分析了1460名高血压患者和1416名非高血压患者 (65岁以上) 的数据.
- 支持矢量机 (SVM) 和粒子优化-支持矢量机 (PSO-SVM) 模型被开发和验证.
- 模型性能使用包括灵敏度,特异性,准确性,F1得分和ROC曲线的曲线下面面积 (AUC) 在内的指标进行评估.
主要成果:
- 与标准SVM相比,PSO-SVM模型表现出更高的性能.
- 对于PSO-SVM的关键性能改进包括灵敏度 (93.9%),F1得分 (0.838) 和AUC-ROC (0.871).
结论:
- 在复杂的分类任务中,PSO-SVM模型被证明是有效的,特别是在高血压检测领域.
- 这种先进的计算方法为老年人高血压的早期诊断和治疗提供了坚实的基础.
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