预测细胞癌:机器学习模型的开发和验证
Tingjin Zheng1, Rong Xu2, Jianming Zhang1
1Department of Clinical Laboratory, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou City, Fujian, China.
Medicine
|January 24, 2026
概括
这项研究开发了一种机器学习模型,使用常规临床数据来预测细胞癌 (RCC). 该模型实现了高精度,有助于早期检测和改善患者的结果.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 细胞癌 (RCC) 在泌尿系统发病率和死亡率方面是一个重大挑战.
- 早期识别RCC对于提高患者预后和治疗疗效至关重要.
研究的目的:
- 使用常规临床数据开发和验证基于机器学习 (ML) 的细胞癌 (RCC) 预测模型.
- 为了确定关键的临床变量,准确的RCC风险评估在风险人群.
主要方法:
- 从2014-2024年回顾性收集临床数据,分为培训 (70%) 和验证 (30%) 队列.
- 评估了七个ML算法,因为其卓越的性能 (AUC) 选择了 eXtreme Gradient Boosting.
- 使用递归特征消除来识别21个显著变量和Shapley添加式解释来解释模型的解释性.
主要成果:
- 最终的ML模型基于极端梯度增强和21个临床变量,在验证队列中实现了0.955的AUC.
- 该模型显示了预测和观察到的风险之间的高度一致性,平均精度为0.923.
- 沙普利的附加解释提供了对特征重要性和个体预测的明确见解.
结论:
- 使用常规临床数据的经过验证的ML模型可以有效地选RCC.
- 这种具有成本效益的方法支持早期RCC检测和干预,可能改善临床结果.
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