基于metaheuristic的胆结石分类使用旋转森林解释与SHAP
Keshika Shrestha1, Proshenjit Sarker1, Jun-Jiat Tiang2
1Electronics and Communication Engineering Discipline, Khulna University, Khulna, Bangladesh.
Frontiers in digital health
|February 2, 2026
概括
这项研究引入了一种新的旋转森林 (RoF) 模型,该模型与白搜索 (BES) 算法进行了优化,用于预测胆结石. 该模型准确地识别了关键的风险因素,使胆结石疾病的早期干预成为可能.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 消化系统疾病研究研究
背景情况:
- 胆结石病 (胆结石病) 影响全球数以百万计的患者,由于症状变化和潜在的严重并发症,如胰腺炎和胆囊癌,导致诊断挑战.
- 早期预测胆结石对于及时的医疗干预和改善患者结果至关重要.
- 现有的诊断方法可能无法完全捕捉胆结石疾病风险因素的复杂性.
研究的目的:
- 开发和评估一种用于早期预测胆结石的新型机器学习方法.
- 为了优化一个旋转森林 (RoF) 分类器,使用 Bald Eagle Search (BES) 算法来提高胆结石预测的准确性.
- 确定胆结石形成的最重要的预测特征.
主要方法:
- 一个表格式数据集被用来训练和测试一个旋转森林 (RoF) 分类器.
- 通过使用 Bald Eagle Search (BES) 算法优化了 RoF 模型,以提高预测性能.
- 使用夏普利添加式解释 (SHAP) 和局部可解释模型不可知解释 (LIME) 分析了特征的重要性.
主要成果:
- 仅使用RoF分类器就实现了78%的准确性和使用所有特征的AUC为0.867.
- 使用BES优化的RoF模型实现了75.78%的准确性和0.860的AUC,使用了17.7的减少特征集.
- 确定的主要预测特征包括C反应蛋白 (CRP),维生素D水平,肥胖,血红蛋白 (HGB) 和体重指数 (BMI).
结论:
- 优化的RoF模型展示了有效的胆结石预测能力.
- 功能重要性分析突出了与胆结石疾病相关的关键生物标志物和临床因素.
- 这种方法为早期胆结石检测和风险分层提供了一个有希望的工具.
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