使用一种基于堆的新型组合分类器检测心血管疾病,具有聚合层,DOWA运算符和特征转换
Mehdi Hosseini Chagahi1, Saeed Mohammadi Dashtaki1, Behzad Moshiri2
1School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Computers in biology and medicine
|April 2, 2024
概括
这项研究引入了一种用于早期发现心血管疾病 (CVD) 的新型机器学习模型. 增强的分类器实现了94.05%的准确性,显著改善了早期诊断和患者的结果.
科学领域:
- 心血管疾病 心血管疾病
- 机器学习 机器学习
- 医学诊断 医学诊断 医学诊断
背景情况:
- 心血管疾病 (CVD) 是一个重大的全球健康挑战,影响生活质量,导致过早死亡.
- 早期检测和干预对于减轻心血管疾病严重程度,进展和死亡率至关重要.
- 机器学习 (ML) 为推进早期心血管疾病检测能力提供了一个有希望的途径.
研究的目的:
- 开发和评估一种基于堆的新型组合分类器,以改善心血管疾病检测.
- 为了提高分类准确性和可靠性,识别心血管疾病病例.
主要方法:
- 使用约翰逊转换的特征转换和特征分布的规范化.
- 一个基于堆的集体分类器,包含一个聚合层和依赖的有序加权平均 (DOWA) 运算符.
- 使用三种不同的第一级分类器和线性支向量机 (SVM) 超分类器进行最终分类.
主要成果:
- 拟议的整体分类器的整体准确率为94.05%,比基线方法提高了5%.
- 该系统显示,接收器运行特征 (ROC) 曲线 (AUC) 下的面积显著增加,达到97.14%.
- 增强分类器在区分阳性和阴性心血管疾病实例方面表现强.
结论:
- 将聚合层添加到堆叠分类器中显著提高了用于心血管疾病检测的分类准确性.
- 与最近的CVD分类研究相比,提出的方法表现出更高的性能和可靠性.
- 开发的分类器具有有效和强大的心血管疾病早期检测的潜力.
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