基于数据的模型对乳腺癌存活率预测的比较分析
Kasahun Takele1,2, Ding-Geng Chen3,4
1Department of Statistics, Haramaya University, Maya, Ethiopia. kastake10@gmail.com.
Scientific reports
|February 21, 2026
概括
这项研究比较了机器学习模型来预测埃塞俄比亚的乳腺癌存活率. 随机生存森林 (RSF) 和随机森林 (RF) 显示了最高的准确性,识别了关键预测因素,如年龄和瘤阶段,以获得更好的患者护理.
科学领域:
- 在瘤学瘤学.
- 数据科学数据科学数据科学
- 生物统计学 生物统计学
背景情况:
- 乳腺癌是一个重大的全球卫生挑战,特别是在低收入和中等收入国家,因为诊断和治疗的机会有限.
- 准确预测乳腺癌存活率对于及时干预和改善患者结果至关重要.
研究的目的:
- 为了比较经典机器学习和生存分析模型在预测埃塞俄比亚乳腺癌存活率方面的表现.
- 确定乳腺癌存活率的关键预测因素,并确保模型可用于临床应用的解释性.
主要方法:
- 追溯分析来自埃塞俄比亚医院的1164名女性治疗数据 (2019-2024年).
- 使用了生存分析方法 (Kaplan-Meier,Cox PH,RSF,DeepSurv) 和机器学习分类器 (SVM,XGBoost,LGBM,RF).
- 使用AUC,C指数和综合障碍得分 (IBS) 进行评估,并使用沙普利增量解释 (SHAP) 进行解释.
主要成果:
- 随机生存森林 (RSF) 和随机森林 (RF) 显示出优异的预测性能 (C指数:0.754;IBS:RSF的0.091).
- SHAP分析确定了年龄,瘤大小,转移,阶段,并发症和婚姻状况作为生存的关键预测因素.
- 射频有效地突出了关键预测因素,而RSF在处理时间到事件数据和审查方面表现出色.
结论:
- 数据驱动的方法,特别是RSF和RF,显著提高了乳腺癌存活率预测的准确性.
- 通过像SHAP这样的可解释模型识别关键的预后因素,有助于精确的风险分层.
- 这项研究强调了高级分析在支持医疗保健专业人员提供及时和知情的患者护理方面的价值.
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