检查诊断应用程序中决策规则机器学习算法的不同成本比率框架.
Sivachandar Kasiviswanathan1, Thulasi Bai Vijayan2
1Department of Electronics and Communication Engineering, RMK College of Engineering and Technology, Puduvoyal, India.
概括
这项研究强调了使用人工智能诊断贫血的成本敏感学习. PART分类器表现出卓越的性能,最大限度地降低了错误分类成本,并为人工智能驱动的医疗保健提供了显著的节省.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 人工智能 (AI) 对于健康诊断至关重要.
- 成本敏感的学习是人工智能诊断的一个关键,经常被忽视的方面.
- 这项研究优先考虑了成本敏感的学习,而不是准确性等其他指标.
研究的目的:
- 为了调查错误分类的总成本,用于决策规则机器学习 (ML) 算法.
- 通过结膜图像和人口统计数据评估ML算法.
- 强调贫血检测的成本敏感分类.
主要方法:
- 使用了基于Java的ML算法:决策表,JRip,OneR和PART.
- 采用了带有结膜图像和人口统计/人类学特征的数据集.
- 应用了10倍交叉验证,并使用四种成本比率 (ρ) 方法分析了成本.
主要成果:
- PART分类器实现了最低的平均总成本 (629.9) 和标准偏差 (335.9).
- 在所有成本比率方法中,PART的表现优于JRip,DecisionTable和OneR.
- 在贫血数据集中,PART分类器的表现一致.
结论:
- 对成本敏感的学习对改善贫血诊断建议具有重要意义.
- 在成本敏感的框架内,PART分类器显示一致的性能.
- 这种方法有可能在人工智能驱动的医疗保健中大幅节省成本.
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