基于机器学习的预测和对中国结直肠癌住院费用的影响因素研究 - 随机森林和支向量回归:一项回顾性研究
1Department of Medical Record Statistics, Peking University Shenzhen Hospital, Shenzhen, China.
Frontiers in public health
|February 23, 2024
概括
预测结直肠癌住院费用对于政策制定至关重要. 主要的程序和停留时间大大影响了成本,随机森林模型的性能优于支持向量回归的准确预测.
科学领域:
- 在瘤学瘤学.
- 卫生经济学 卫生经济学
- 在医疗保健中的数据科学.
背景情况:
- 结肠直肠癌发病率的上升和相关的高住院费用需要成本预测.
- 准确的成本预测可以为医疗保健成本控制和政策制定提供信息.
研究的目的:
- 确定影响结肠直肠癌患者住院费用的关键因素.
- 使用机器学习算法开发和比较这些成本的预测模型.
主要方法:
- 对结直肠癌患者住院病历数据的分析 (2018-2022年).
- 应用随机森林和支持向量回归模型来预测住院费用.
- 使用R平方和根平均平方误差 (RMSE) 评估模型性能.
主要成果:
- 确定了影响成本的关键因素:主要程序,停留时间,程序级别,查尔森并发症指数,年龄和支付方式.
- 与支持向量回归 (R2=0.824,RMSE=0.032) 相比,随机森林模型获得了更高的准确性 (R2=0.833,RMSE=0.029).
- 主要程序在随机森林模型的预测中显示了最高的权重 (0.286).
结论:
- 大型手术和住院时间是结直肠癌住院费用的主要驱动因素.
- 随机森林模型展示了比支持向量回归对结直肠癌住院费用的优越预测能力.
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