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一个用于预测心脏病的混合框架,使用经典和量子启发的机器学习技术
Ankur Kumar1, Sanjay Dhanka2, Abhinav Sharma2
1School of Computing and Electrical Engineering (SCEE), Indian Institute of Technology (IIT) Mandi, Mandi, 175005, Himachal Pradesh, India.
Scientific reports
|July 11, 2025
概括
这项研究引入了一种混合框架,将经典和量子启发的机器学习结合起来,以改善心脏病预测. 这种新的方法通过使用集成数据集和先进的优化技术来提高预测的准确性和稳定性.
科学领域:
- 心血管健康 心血管健康
- 机器学习 机器学习
- 计算科学 计算科学
背景情况:
- 准确的心脏病预测对于及时干预和改善患者结果至关重要.
- 现有的机器学习模型在预测准确性和稳定性方面面临挑战.
- 整合多样化的数据集和先进的计算技术可能可以克服这些局限性.
研究的目的:
- 提出和评估一种用于心脏病预测的新型混合框架.
- 整合经典和量子启发的机器学习技术,以提高性能.
- 将拟议框架与最先进的方法进行比较.
主要方法:
- 开发了一个混合框架,结合了经典和量子启发的机器学习模型.
- 来自多个心脏病数据集 (克利夫兰,匈牙利,瑞士,长,Statlog) 的数据被组合和预处理.
- 支持矢量机 (SVM) 分类器使用遗传算法 (CGA),粒子群优化 (CPSO),量子遗传算法 (QGA) 和量子粒子群优化 (QPSO) 进行了训练和优化.
- 使用十倍交叉验证来评估使用各种指标的性能,包括准确性,F1得分,精度,灵敏度,特异性和诊断几率比率 (DOR).
主要成果:
- 与现有方法相比,混合框架证明了心脏病预测准确度和稳定性的提高.
- 经典模型和量子模型都表现出了竞争力的表现,混合方法提供了增强的功能.
- 特性选择和严格的交叉验证确保了可靠的模型评估.
结论:
- 拟议的混合框架整合了经典和量子启发的机器学习,显示了促进心脏病预测的巨大潜力.
- 这种新的方法提供了一种强大而准确的方法来识别心脏病风险较高的人.
- 进一步的研究可以探索这种混合框架在临床环境中的更广泛应用.
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