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量子启发的海优化了对心血管疾病预测的深信网络方法
D Banumathy1, T Vetriselvi2, K Venkatachalam3
1Department of Computer Science and Engineering, Paavai Engineering College, Namakkal, India.
PeerJ. Computer science
|February 3, 2025
概括
这项研究引入了一种量子启发的海优化算法 (QISOA) 与深度信念网络 (DBN) 以改善心血管疾病预测. 新的QISOA-DBN模型实现了98.6%的准确性,超过了现有的方法.
科学领域:
- 心血管疾病研究研究
- 医疗保健中的人工智能
- 计算生物学 计算生物学
背景情况:
- 早期发现心血管疾病对于降低死亡率至关重要.
- 现有的诊断模型经常面临准确性和效率方面的挑战.
- 需要先进的机器学习技术来增强心血管疾病的识别.
研究的目的:
- 为改善心血管疾病预测提出一种新的混合模型.
- 使用人工智能提高心血管疾病识别的准确性和效率.
- 为了利用量子启发的优化,提高机器学习模型的性能.
主要方法:
- 数据预处理:克利夫兰心脏病数据集的清洁,转换和标准化.
- 模型开发:将深度信念网络 (DBN) 与量子启发的海优化算法 (QISOA) 结合起来,以优化权重和偏差.
- 性能评估:比较QISOA-DBN模型与传统的机器学习和元启发算法.
主要成果:
- 经QISOA优化的DBN模型实现了98.6%的高精度.
- 该模型表现出卓越的精度 (97.6%),回忆 (96.8%) 和F1得分 (97.1%).
- 在心血管疾病预测中表现优于传统的机器学习,元启发算法和最新的方法.
结论:
- 拟议的QISOA-DBN模型显著提高了心血管疾病预测的准确性.
- 量子启发的优化增强了DBN的功能学习和分类能力.
- 这种混合方法为早期和准确诊断心血管疾病提供了一个有希望的工具.
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