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通过集体机器学习改善乙型肝炎结果预测:对预测模型和可解释性的研究.

Abid Bin Ahosan¹, Forhadul Islam¹, Khandaker Mohammad Mohi Uddin²

¹Department of Computer Science and Engineering, Dhaka International University, Dhaka, Bangladesh.

|June 19, 2025

概括

机器学习准确预测B型肝炎病毒 (HBV) 患者的结果. 结合支持矢量机和物流回归等模型,准确度提高到95%,帮助制定更好的患者护理策略.

关键词:

乙型肝炎 B 型肝炎人工智能的人工智能是人工智能.模型可解释性模型可解释性有关风险因素的风险因素.

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科学领域:

医疗信息学医疗信息学
计算生物学计算生物学
公共卫生公共卫生

背景情况:

乙型肝炎病毒 (HBV) 感染对全球健康构成重大挑战,导致肝硬化和肝癌等严重肝病.
早期诊断和干预至关重要,特别是在资源有限的地区,以减少HBV的影响.
预测建模可以提高患者管理和HBV感染的结果.

研究的目的:

评估各种机器学习 (ML) 技术的有效性,以预测B型肝炎病毒 (HBV) 感染患者的结果.
确定与HBV患者死亡相关的关键风险因素.
提高用于HBV结果预测的ML模型的可解释性.

主要方法:

使用Chi-平方测试进行了特征选择.
使用合成少数人过量采样技术 (SMOTE) 来解决阶级不平衡问题.
机器学习模型包括支持向量机 (SVM),后勤回归 (LR) 和投票分类器进行了训练和评估. 使用SHAP和LIME增强了模型的解释性.

主要成果:

单个模型,支持矢量机 (SVM) 和后勤回归 (LR),实现了92.5%的准确性.
结合SVM和LR的投票分类器将预测准确度提高到95%.
某些风险因素的升高水平,特别是在老年人中,与死亡风险增加有显著的关联.

结论:

机器学习模型在预测HBV患者结果方面表现出很高的准确性.
这些发现强调了特定风险因素和患者年龄在预后确定中的重要性.
这些预测性见解可以为管理HBV感染的临床决策和公共卫生策略提供信息.