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优化整体机器学习模型以准确预测医疗保健中的肝病

W El Atifi^1,2, O El Rhazouani¹, Fida Muhammad Khan³

¹Hassan First University of Settat, High Institute of Health Sciences, Laboratory of Sciences and Health Technologies, Settat, Morocco.

|August 28, 2025

概括

机器学习模型对早期肝病检测有前途. 随机森林分类器的准确度超过85%,为肝脏疾病提供了潜在的新诊断工具.

科学领域:

医疗信息学
计算生物学
机器学习

背景情况:

肝病包括肝炎和肝硬化等疾病,
危险因素包括感染,酒,肥胖和遗传倾向.
有效的治疗依赖于早期检测和生活方式的改变, 以防止严重的肝损伤.

研究的目的:

评估整体机器学习分类器在预测肝病方面的有效性.
使用特定数据集比较随机森林,Ada Boost和梯度提升分类器的性能.
确定最佳的机器学习模型来准确诊断肝脏疾病.

主要方法:

在数据集上进行特征提取和选择.
使用随机搜索CV和GridSearchCV进行超参数调整.
实施和比较了包括随机森林,Ada Boost和梯度提升在内的整体分类器.

主要成果:

通过GridSearchCV优化的随机森林分类器实现了最高准确度超过85.17%.
该模型表现出平衡的性能,精度为0.85,回忆率为0.81 (存在) 和0.87 (不存在),F1分数为0.83和0.85.
艾达提升和梯度提升分类器也表现出很高的表现,但表现并没有明显超过随机森林.

结论:

整体机器学习技术显示出诊断肝脏疾病的巨大潜力.
优化的随机森林分类器可以作为精确的诊断工具,改善患者的治疗结果.
用更大的数据集和深度学习集成进行进一步的研究是有必要的,以推进医疗诊断中的ML应用.

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