Fibrosis and inflammatory activity diagnosis of chronic hepatitis C based on extreme learning machine
Abstract 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 f...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-84695-4 |
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| author | Jiaxin Cai Tingting Chen Yang Qi Siyu Liu Rongshang Chen |
| author_facet | Jiaxin Cai Tingting Chen Yang Qi Siyu Liu Rongshang Chen |
| author_sort | Jiaxin Cai |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-9fb068f776da4db6819f33d8f93c5162 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9fb068f776da4db6819f33d8f93c51622025-01-05T12:20:09ZengNature PortfolioScientific Reports2045-23222025-01-0115111310.1038/s41598-024-84695-4Fibrosis and inflammatory activity diagnosis of chronic hepatitis C based on extreme learning machineJiaxin Cai0Tingting Chen1Yang Qi2Siyu Liu3Rongshang Chen4School of Mathematics and Statistics, Xiamen University of TechnologySchool of Mathematics and Statistics, Xiamen University of TechnologySchool of Computer and Information Engineering, Xiamen University of TechnologySchool of Computer and Information Engineering, Xiamen University of TechnologySchool of Computer and Information Engineering, Xiamen University of TechnologyAbstract 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.https://doi.org/10.1038/s41598-024-84695-4 |
| spellingShingle | Jiaxin Cai Tingting Chen Yang Qi Siyu Liu Rongshang Chen Fibrosis and inflammatory activity diagnosis of chronic hepatitis C based on extreme learning machine Scientific Reports |
| title | Fibrosis and inflammatory activity diagnosis of chronic hepatitis C based on extreme learning machine |
| title_full | Fibrosis and inflammatory activity diagnosis of chronic hepatitis C based on extreme learning machine |
| title_fullStr | Fibrosis and inflammatory activity diagnosis of chronic hepatitis C based on extreme learning machine |
| title_full_unstemmed | Fibrosis and inflammatory activity diagnosis of chronic hepatitis C based on extreme learning machine |
| title_short | Fibrosis and inflammatory activity diagnosis of chronic hepatitis C based on extreme learning machine |
| title_sort | fibrosis and inflammatory activity diagnosis of chronic hepatitis c based on extreme learning machine |
| url | https://doi.org/10.1038/s41598-024-84695-4 |
| work_keys_str_mv | AT jiaxincai fibrosisandinflammatoryactivitydiagnosisofchronichepatitiscbasedonextremelearningmachine AT tingtingchen fibrosisandinflammatoryactivitydiagnosisofchronichepatitiscbasedonextremelearningmachine AT yangqi fibrosisandinflammatoryactivitydiagnosisofchronichepatitiscbasedonextremelearningmachine AT siyuliu fibrosisandinflammatoryactivitydiagnosisofchronichepatitiscbasedonextremelearningmachine AT rongshangchen fibrosisandinflammatoryactivitydiagnosisofchronichepatitiscbasedonextremelearningmachine |