应用机器学习方法研究影响肝炎患者生存结果的预测因素。

IF 1.6 4区医学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-08-31 DOI:10.1080/10255842.2025.2546917

Xiaohua Li, Minghong Yang

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引用次数: 0

摘要

由A-E型病毒引起的肝炎可以悄无声息地发展为肝损伤、肝硬化或癌症。慢性B和C增加衰竭风险。机器学习模型利用患者数据、症状和病史帮助预测肝炎风险。本研究采用决策树分类（DTC）和极端梯度增强分类（XGBC），并结合根茎切分优化算法（ROA）、淘金优化算法（GRO）和运动编码带电粒子优化算法（MEPO）三种优化算法来提高准确率。其中DTRO的准确率最高（0.991），优于DTC。XGRO次之，0.991，DTME次之，0.954。DTRO成为预测肝炎生存最可靠的模型。

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Predictive factors affecting hepatitis patients survival results via application of the machine learning methods.

Hepatitis, caused by viruses A-E, can silently progress to liver damage, cirrhosis, or cancer. Chronic B and C increase failure risk. Machine learning models help predict hepatitis risks using patient data, symptoms, and history. This study used Decision Tree Classification (DTC) and Extreme Gradient Boosting Classification (XGBC) with three optimizers Rhizotomy Optimization Algorithm (ROA), Gold Rush Optimizer (GRO), and Motion-Encoded Electric Charged Particles Optimization Algorithm (MEPO) to enhance accuracy. Among hybrids, DTRO achieved the highest accuracy (0.991), outperforming DTC. XGRO followed with 0.991, and DTME with 0.954. DTRO emerged as the most reliable model for predicting hepatitis survival.

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来源期刊

Computer Methods in Biomechanics and Biomedical Engineering 工程技术-工程：生物医学

CiteScore

4.10

自引率

6.20%

发文量

179

审稿时长

4-8 weeks

期刊介绍： The primary aims of Computer Methods in Biomechanics and Biomedical Engineering are to provide a means of communicating the advances being made in the areas of biomechanics and biomedical engineering and to stimulate interest in the continually emerging computer based technologies which are being applied in these multidisciplinary subjects. Computer Methods in Biomechanics and Biomedical Engineering will also provide a focus for the importance of integrating the disciplines of engineering with medical technology and clinical expertise. Such integration will have a major impact on health care in the future.