机器学习可解释鼻癌存活率使用LIME和SHAP
Rasheed Omobolaji Alabi1,2, Mohammed Elmusrati3, Ilmo Leivo4
1Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland. rasheed.alabi@helsinki.fi.
Scientific reports
|June 2, 2023
概括
这项研究开发了一种机器学习 (ML) 模型,用于预测鼻癌 (NPC) 患者的生存率. ML模型将患者分为低生存率和高生存率组,有助于个性化治疗规划.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 生物统计学 生物统计学
背景情况:
- 鼻癌 (NPC) 具有独特的组织病理学和可变的患者结果.
- 准确的预后对于有效的治疗计划和NPC患者管理至关重要.
研究的目的:
- 开发一种机器学习 (ML) 预后系统,将NPC患者分为不同的生存群体.
- 通过可解释的人工智能 (XAI) 技术提高模型的解释性.
主要方法:
- 开发并使用SEER数据库和外部队列验证了结合五种算法的堆叠ML模型.
- 极端梯度提升 (XGBoost) 被用作比较的基准.
- 对于模型的可解释性,使用了局部可解释的模型不可知解释 (LIME) 和夏普利添加式解释 (SHAP).
主要成果:
- 堆叠的ML模型实现了85.9%的准确性,在训练和测试中表现优于XGBoost (84.5%).
- 对XGBoost的外部验证显示c指数为0.74,准确度为76.7%,AUC为0.76.
- SHAP分析确定了年龄,T阶段,种族,M阶段,婚姻状况和年级作为NPC生存的关键预测因素.
结论:
- 开发的ML模型准确地预测了NPC患者的整体存活率.
- XAI技术提供个性化的风险因素,并揭示非线性关系,支持个性化治疗.
- 这种ML方法可以显著帮助临床决策,并提高NPC的治疗结果.
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