疾病预测的集体学习:一篇评论
Palak Mahajan1, Shahadat Uddin2, Farshid Hajati1
1College of Engineering and Science, Victoria University, Sydney, NSW 2000, Australia.
Healthcare (Basel, Switzerland)
|June 28, 2023
概括
堆叠组合模型显示,与袋装,提升和投票相比,疾病预测的准确性更高. 这篇评论强调了用于诊断糖尿病和心脏病等疾病的机器学习趋势.
科学领域:
- * 计算生物学和生物信息学
- * 医疗信息学和机器学习
- *健康数据分析.
背景情况:
- * 机器学习模型增强了疾病预测框架.
- *合体学习结合了多个分类器,以提高准确性.
- *对常见疾病的组合方法的评估有限.
研究的目的:
- * 评估疾病预测的组合技术 (包装,提升,堆叠,投票).
- * 确定针对五种主要疾病的性能趋势:糖尿病,皮肤,脏,肝脏和心脏病.
- * 为选择最佳预测模型提供见解.
主要方法:
- *对2016-2023年研究进行系统的文献搜索.
- *确定了45篇文章,其中至少有两种综合方法应用于目标疾病.
- *在不同组合方法中对性能准确性的比较分析.
主要成果:
- * 堆叠,尽管应用较少 (23),但最经常 (19/23) 产生最高准确度.
- *投票是第二个表现最好的合奏方法.
- *包装在脏疾病的预测中表现出色;在肝脏和糖尿病中提高.
- * 堆叠始终显示皮肤和糖尿病的最佳性能.
结论:
- * 堆叠显示在疾病预测中比其他组合方法更高的准确性.
- * 性能在不同组合方法和疾病数据集之间存在显著差异.
- * 结果指导研究人员在选择预测分析的合适组合模型.
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