由人工智能驱动的 CardioPredict:用于监测心脏健康的协同整体框架
Hemalata Nawale1, Mangesh Nikose1
1Department of Electrical and Electronics Engineering, Sandip University, Nashik, Maharashtra, India.
The International journal of artificial organs
|November 4, 2025
概括
这项研究引入了一种混合机器学习框架,用于准确预测心脏病,克服缺失值和不平衡等数据挑战. 这种新的方法显著提高了诊断的可靠性和效率.
科学领域:
- 心血管医学 心血管医学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 心脏病仍然是全球主要的死亡原因.
- 现有的机器学习模型在预处理,特征选择和用于心脏病预测的超参数调整方面面临挑战.
- 这些局限性导致偏见和计算密集型诊断模型.
研究的目的:
- 开发一个强大的混合框架,以改善心脏病预测.
- 解决数据预处理的挑战,包括缺失的数据,类不平衡和特征选择.
- 通过先进的技术来提高模型的准确性和效率.
主要方法:
- 一个平衡的推算-规范化框架,使用K-近邻 (KNN) 推算和StandardScaler规范化.
- 合成少数群体过量采样技术 (SMOTE) 处理阶级不平衡.
- 匈牙利算法用于特征选择和贝叶斯优化用于超参数调整.
- 组合学习将随机森林,决策树,KNN,天真贝叶斯和极端梯度提升 (XGBoost) 与投票组合相结合.
主要成果:
- 拟议的框架有效地处理缺失的数据和阶级不平衡.
- 在基准数据集上,XGBoost实现了96.43%的峰值精度.
- 投票组合模型以95.66%的准确性表现出强的表现.
- 混合方法在准确性和计算效率方面明显优于传统模型.
结论:
- 开发的混合框架为心脏病预测提供了可靠和高效的解决方案.
- 集体学习和先进的数据预处理技术对于提高诊断准确性至关重要.
- 这种方法有可能提高心脏病的早期检测和管理.
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