Cai Jiaxin, Chen Tingting, Qi Yang, Liu Siyu, Chen Rongshang
School of Mathematics and Statistics, Xiamen University of Technology, Xiamen, 361024, China.
School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, 361024, China.
Sci Rep. 2025 Jan 2;15(1):11. doi: 10.1038/s41598-024-84695-4.
The traditional diagnosis of chronic hepatitis C usually relies on liver biopsy. Diagnosing chronic hepatitis C based on serum indices provides a non-invasive way to determine the stage of chronic hepatitis C without liver biopsy. In this paper, we proposed two automatic diagnosis systems for non-invasive diagnosis of chronic hepatitis C based on serum indices, an extreme learning machine (ELM) based auto-diagnosis method and a hybrid method using k-means clustering and ELM. The two proposed systems were used to predict the fibrosis stage and inflammatory activity grade of patients with chronic hepatitis C by analyzing their serum index observations. ELM has superiorities such as simple structure and fast calculation speed and can provide good diagnosis performance. To overcome the problem of class-imbalance, outliers and small sample size, we also proposed a method hybridizing k-means and ELM. It employed the k-means clustering to generate new robust training samples and then employed the new generated training samples to train an ELM for chronic hepatitis C diagnosis. The proposed methods were tested on 123 real clinical cases. Experimental results show that the proposed methods outperform the state-of-the-art methods for the fibrosis stage and inflammatory activity grade diagnosis tasks.
慢性丙型肝炎的传统诊断通常依赖于肝活检。基于血清指标诊断慢性丙型肝炎提供了一种无需肝活检就能确定慢性丙型肝炎分期的非侵入性方法。在本文中，我们提出了两种基于血清指标的慢性丙型肝炎非侵入性诊断自动诊断系统，一种基于极限学习机（ELM）的自动诊断方法和一种使用k均值聚类与ELM的混合方法。通过分析慢性丙型肝炎患者的血清指标观测值，所提出的两种系统被用于预测患者的纤维化分期和炎症活动度分级。ELM具有结构简单、计算速度快等优势，并且能够提供良好的诊断性能。为了克服类别不平衡、离群值和小样本量的问题，我们还提出了一种将k均值和ELM相结合的方法。它利用k均值聚类生成新的稳健训练样本，然后利用新生成的训练样本训练一个用于慢性丙型肝炎诊断的ELM。所提出的方法在123个真实临床病例上进行了测试。实验结果表明，所提出的方法在纤维化分期和炎症活动度分级诊断任务上优于现有方法。
