胆结石分类使用随机森林优化沙猫群优化算法与SHAP和基于DiCE的可解释性.
Proshenjit Sarker1, Jun-Jiat Tiang2, Abdullah-Al Nahid1
1Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究引入了一个优化的随机森林分类器,使用沙猫群优化用于从临床数据中预测胆结石. 该模型有效地识别了CRP,维生素D和AAST等关键指标,改善了诊断潜力.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 计算生物学 计算生物学
背景情况:
- 胆结石病影响全球10-20%的成年人,需要早期诊断.
- 机器学习 (ML) 对胆结石检测有希望,但表式数据方法尚未得到充分探索.
- 用临床数据优化诊断模型对于有效的胆结石管理至关重要.
研究的目的:
- 开发和评估一种随机森林 (RF) 分类器,该分类器与沙猫群优化 (SCSO) 进行了优化,用于使用表式临床数据预测胆结石.
- 评估交叉验证和特征减少对模型性能的影响.
- 识别关键的预测特征并提高模型的可解释性.
主要方法:
- 一个随机森林 (RF) 分类器使用沙猫群优化 (SCSO) 算法被开发和优化.
- 实验使用四个框架进行:没有交叉验证的RF (CV),带有CV的RF,没有CV的RF-SCSO和带有CV的RF-SCSO.
- 使用SHAP分析了特征的重要性,使用DiCE增强了模型的解释性.
主要成果:
- 射频-SCSO模型实现了与标准射频模型相似的准确性,同时显著减少了功能集从38到13.
- SHAP分析确定了C反应蛋白 (CRP),维生素D和阿斯巴胺转移酶 (AAST) 作为具有高度影响力的特征.
- 对于错误分类的实例,DiCE分析提供了纠正的反事实,提高了模型的透明度.
结论:
- 优化的ML模型,特别是RF-SCSO,通过结构化临床数据为胆结石诊断提供了可行的方法.
- 像SHAP和DiCE这样的特征选择和可解释性方法对于完善诊断ML模型是有价值的.
- 这项研究强调了表格数据和可解释的ML在改善胆结石疾病管理方面的潜力.
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