可解释的甲状腺癌诊断通过两级机器学习优化和改进的裸体鼠算法
1Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Cracow, Poland.
Cancers
|January 8, 2025
概括
这项研究增强了机器学习,用于使用裸鼠算法 (NMRA) 诊断甲状腺瘤恶性瘤. 优化的LightGBM模型实现了81.82%的准确性,改善了癌症诊断.
科学领域:
- 医疗信息学 医疗信息学
- 在瘤学中使用人工智能
- 生物启发的计算 生物启发的计算
背景情况:
- 机器学习 (ML) 显示了癌症诊断的前景.
- 提高ML模型对于提高诊断准确性至关重要.
- 甲状腺瘤恶性瘤评估需要强大的诊断工具.
研究的目的:
- 改进使用ML的甲状腺瘤恶性瘤分类.
- 应用裸鼠算法 (NMRA) 进行ML模型优化.
- 为了评估NMRA增强的ML分类器的性能.
主要方法:
- 利用了来自京医院的2022年数据集 (1232条记录,19个特征).
- 应用了10ML分类器 (例如XGBoost,LightGBM,随机森林).
- 在分类器中使用NMRA进行参数优化和特征选择.
主要成果:
- 优化NMRA的LightGBM模型实现了最高的精度 (81.82%) 和F1得分 (86.62%).
- NMRA有效地优化了参数,并选择了相关特征以改进分类.
- SHAP值为LightGBM模型的决策提供了解释性.
结论:
- NMRA显著提高了甲状腺瘤恶性病症诊断的ML模型性能.
- 优化的LightGBM为准确的癌症诊断提供了一个强大的工具.
- 可解释性AI (SHAP) 对于理解诊断模型行为是有价值的.
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