超越传统模型:为准确预测心脏病的Jaya优化合奏
Sashikanta Prusty1, Shyam Sunder Goud2, Jyotirmayee Rautaray2
1Department of Computer Science and Engineering, ITER-FET, Siksha 'O' Anusandhan (deemed to be University), Bhubaneswar, 751030, India.
Computers in biology and medicine
|July 17, 2025
概括
这项研究引入了一种新的Jaya优化堆组合 (J-oSE) 方法,用于准确地分类心脏病 (HD). J-oSE方法显著提高了诊断准确性,为早期检测和患者护理提供了更可靠的工具.
科学领域:
- 心脏病学 心脏病学
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 心脏病 (HD) 是全球主要的死亡原因.
- 准确及时检测HD对于改善患者生存率至关重要.
- 现有的诊断方法需要提高精度和效率.
研究的目的:
- 开发和评估用于心脏病分类的新型组合方法.
- 将Jaya优化技术与堆叠组合模型集成.
- 为了提高基于机器学习的HD检测的准确性和稳定性.
主要方法:
- 用两个不同的心脏病数据集进行分析.
- 应用数据预处理和传统机器学习模型进行初始预测.
- 开发了一个Jaya优化堆组合 (J-oSE) 模型用于最终分类.
- 使用混矩阵,ROC曲线,10倍交叉验证和统计t测试来评估性能.
主要成果:
- J-oSE方法的准确性很高:第一个数据集的准确性为93.55%,第二个数据集的准确性为84.88%.
- 性能超过了现有的方法,如Voting Ensemble,XGBoost和基线堆叠.
- 统计分析 (p值,t统计) 证实了Jaya优化的J-oSE方法的优越性和稳定性.
结论:
- 拟议的Jaya优化堆叠方法为心脏病分类提供了可靠和强大的方法.
- 这种方法平衡了诊断的准确性,最大限度地降低了错误的阳性或阴性诊断的风险.
- J-oSE方法显示了在心血管健康领域推进预测性能的潜力.
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