启发性预后:使用可解释的机器学习方法预测肝炎.
Niloy Das1, Md Bipul Hossain1, Apurba Adhikary1
1Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali, Chittagong, Bangladesh.
PloS one
|April 2, 2025
概括
机器学习模型可以准确预测肝炎感染. 支持矢量机 (SVM) 模型实现了99.25%的准确性,在早期肝炎检测和患者管理方面取得了重大进展.
科学领域:
- 肝病学和计算机医学
- 医疗保健中的人工智能
背景情况:
- 肝炎是一种普遍的炎症性肝病,对全球健康造成重大负担.
- 目前的肝炎检测方法有局限性,需要新的诊断策略.
- 早期发现肝炎对于有效的患者管理和改善结果至关重要.
研究的目的:
- 调查传统机器学习 (ML) 分类器对肝炎感染预测的有效性.
- 为了比较物流回归,SVM,决策树,随机森林和MLP模型的性能.
- 为了确定早期肝炎检测最有效的ML方法.
主要方法:
- 广泛的数据预处理:异常值检测,数据集平衡和特征工程.
- 使用默认超参数,调整 GridSearchCV 和组合技术评估 ML 模型.
- 通过准确性,精度,回忆,F1测量,AUC和5倍交叉验证进行性能评估.
主要成果:
- 支持矢量机 (SVM) 模型实现了99.25%的准确性,99.27%的精度和99.24%的回忆/F1测量,完美的AUC为1.00.
- 多层感知器 (MLP) 和随机森林模型也显示出高精度 (99.00%).
- 可解释性分析确定了肝炎检测的关键特征,验证了模型性能.
结论:
- 机器学习,特别是SVM,为肝炎感染预测提供了一个高度准确和强大的方法.
- 拟议的ML模型在肝炎检测准确性和指标方面明显优于现有方法.
- 这项研究强调了人工智能在促进早期肝炎诊断和管理方面的潜力.
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