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基于混合遗传算法驱动的机器学习模型的优化,用于预测心脏病的预测
Sherko H Murad1, Noor Bahjat Tayfor2, Nozad H Mahmood3
1Computer Science Department, Cihan University of Sulaimaniya, Sulaymaniyah, Kurdistan, Iraq.
MethodsX
|July 30, 2025
概括
基因算法 (GA) 优化显著增强了用于心脏病预测的机器学习模型. 这种方法使K-近邻 (KNN) 精度提高到95.38%,支持向量机 (SVM) 提高到90%.
科学领域:
- 心脏病学 心脏病学
- 计算机科学 计算机科学
- 生物信息学是一种生物信息学.
背景情况:
- 像KNN和SVM这样的机器学习模型对于心脏病预测至关重要.
- 超参数选择显著影响这些ML模型的性能.
- 现有的方法往往难以实现最佳的超参数调整.
研究的目的:
- 引入一种新的混合方法,使用遗传算法 (GA) 来优化KNN和SVM超参数.
- 提高ML模型对心脏病预测的分类准确性和临床相关性.
- 系统地提高机器学习模型的预测性能.
主要方法:
- 利用基因算法 (GA) 系统地优化K-最接近邻居 (KNN) 和支持矢量机 (SVM) 分类器的超参数.
- 基于关键分类指标评估模型性能,包括准确性,精度,回忆和F-score.
- 应用GA驱动的超参数调整来改善预测结果.
主要成果:
- 基于GA的超参数调整使KNN和SVM模型的性能得到了大幅改善.
- 获得了KNN的95.38%和SVM的90%的分类准确度.
- 对优化模型的精度,回忆和F-score有显著的改进.
结论:
- 基于GA的超参数调整是提高ML模型在心脏病分类中的性能的一个有效策略.
- 拟议的混合方法为心脏病诊断提供了更好的预测能力和临床相关性.
- 优化的ML模型显示了更准确,更可靠的心脏病预测的潜力.
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