最佳机器学习模型用于开发晚期癌症患者的预后预测
Jun Hamano1, Ayano Takeuchi2, Tomoya Keyaki3
1Palliative and Supportive Care, University of Tsukuba, Tsukuba, JPN.
Cureus
|January 23, 2025
概括
机器学习模型,如内核支持向量机 (KSVM),在预测晚期癌症患者的30天生存期方面显示出高准确性. 传统模型提供了稳定性,突出了在息治疗中需要数据驱动的模型选择的需要.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 准确的预后对于癌症患者的护理至关重要,特别是在息环境中.
- 机器学习 (ML) 模型越来越多地被使用,但它们与癌症预后的传统统计模型的比较尚未得到充分研究.
研究的目的:
- 为了比较统计和ML模型的预后准确性,以预测晚期癌症患者的30天生存期.
- 用客观临床数据评估模型性能,包括血液检测结果.
主要方法:
- 日本-预测评估工具验证 (J-ProVal) 研究 (2012-2014) 的二次分析.
- 包括来自日本58个息护理服务的915名患者.
- 通过使用17个客观临床特征,比较了四种模型:分数多项式 (FP) 回归,KFDA,KSVM和XGBoost,使用17个客观临床特征.
- 主要评估指标是接收器操作特征曲线 (AUC) 下的面积.
主要成果:
- 内核支持向量机 (KSVM) 实现了最高的预测准确性 (AUC:0.834).
- KSVM的表现优于分数多项式 (FP) 回归 (AUC:0.799).
- XGBoost的性能较低,可能是由于数据集大小的限制.
结论:
- 机器学习,特别是KSVM,在有足够的数据的情况下,对息护理生存的高预测准确度.
- 传统的统计模型在稳定性和可解释性方面具有优势.
- 模型选择应根据特定的数据特征进行量身定制,以实现最佳的预后预测.
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