Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction
Abstract Hepatitis C, a particularly dangerous form of viral hepatitis caused by hepatitis C virus (HCV) infection, is a major socio-economic and public health problem. Due to the rapid development of deep learning, it has become a common practice to apply deep learning to the healthcare industry to...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-59785-y |
| _version_ | 1797199418562183168 |
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| author | Lin Zhang Jixin Wang Rui Chang Weigang Wang |
| author_facet | Lin Zhang Jixin Wang Rui Chang Weigang Wang |
| author_sort | Lin Zhang |
| collection | DOAJ |
| description | Abstract Hepatitis C, a particularly dangerous form of viral hepatitis caused by hepatitis C virus (HCV) infection, is a major socio-economic and public health problem. Due to the rapid development of deep learning, it has become a common practice to apply deep learning to the healthcare industry to improve the effectiveness and accuracy of disease identification. In order to improve the effectiveness and accuracy of hepatitis C detection, this study proposes an improved denoising autoencoder (IDAE) and applies it to hepatitis C disease detection. Conventional denoising autoencoder introduces random noise at the input layer of the encoder. However, due to the presence of these features, encoders that directly add random noise may mask certain intrinsic properties of the data, making it challenging to learn deeper features. In this study, the problem of data information loss in traditional denoising autoencoding is addressed by incorporating the concept of residual neural networks into an enhanced denoising autoencoder. In our experimental study, we applied this enhanced denoising autoencoder to the open-source Hepatitis C dataset and the results showed significant results in feature extraction. While existing baseline machine learning methods have less than 90% accuracy and integrated algorithms and traditional autoencoders have only 95% correctness, the improved IDAE achieves 99% accuracy in the downstream hepatitis C classification task, which is a 9% improvement over a single algorithm, and a nearly 4% improvement over integrated algorithms and other autoencoders. The above results demonstrate that IDAE can effectively capture key disease features and improve the accuracy of disease prediction in hepatitis C data. This indicates that IDAE has the potential to be widely used in the detection and management of hepatitis C and similar diseases, especially in the development of early warning systems, progression prediction and personalised treatment strategies. |
| first_indexed | 2024-04-24T07:15:26Z |
| format | Article |
| id | doaj.art-b365e86c8a1242cf8bd98a8818eb882f |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T07:15:26Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b365e86c8a1242cf8bd98a8818eb882f2024-04-21T11:19:10ZengNature PortfolioScientific Reports2045-23222024-04-0114111210.1038/s41598-024-59785-yInvestigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C predictionLin Zhang0Jixin Wang1Rui Chang2Weigang Wang3Zhejiang Hospital of Integrated Traditional Chinese and Western MedicineDepartment of Statistics and Mathematics, Zhejiang Gongshang UniversityDepartment of ICU, Jining No.1 People’s HospitalDepartment of Statistics and Mathematics, Zhejiang Gongshang UniversityAbstract Hepatitis C, a particularly dangerous form of viral hepatitis caused by hepatitis C virus (HCV) infection, is a major socio-economic and public health problem. Due to the rapid development of deep learning, it has become a common practice to apply deep learning to the healthcare industry to improve the effectiveness and accuracy of disease identification. In order to improve the effectiveness and accuracy of hepatitis C detection, this study proposes an improved denoising autoencoder (IDAE) and applies it to hepatitis C disease detection. Conventional denoising autoencoder introduces random noise at the input layer of the encoder. However, due to the presence of these features, encoders that directly add random noise may mask certain intrinsic properties of the data, making it challenging to learn deeper features. In this study, the problem of data information loss in traditional denoising autoencoding is addressed by incorporating the concept of residual neural networks into an enhanced denoising autoencoder. In our experimental study, we applied this enhanced denoising autoencoder to the open-source Hepatitis C dataset and the results showed significant results in feature extraction. While existing baseline machine learning methods have less than 90% accuracy and integrated algorithms and traditional autoencoders have only 95% correctness, the improved IDAE achieves 99% accuracy in the downstream hepatitis C classification task, which is a 9% improvement over a single algorithm, and a nearly 4% improvement over integrated algorithms and other autoencoders. The above results demonstrate that IDAE can effectively capture key disease features and improve the accuracy of disease prediction in hepatitis C data. This indicates that IDAE has the potential to be widely used in the detection and management of hepatitis C and similar diseases, especially in the development of early warning systems, progression prediction and personalised treatment strategies.https://doi.org/10.1038/s41598-024-59785-yHepatitis CAutoencoderDenoising autoencoder |
| spellingShingle | Lin Zhang Jixin Wang Rui Chang Weigang Wang Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction Scientific Reports Hepatitis C Autoencoder Denoising autoencoder |
| title | Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction |
| title_full | Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction |
| title_fullStr | Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction |
| title_full_unstemmed | Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction |
| title_short | Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction |
| title_sort | investigation of the effectiveness of a classification method based on improved dae feature extraction for hepatitis c prediction |
| topic | Hepatitis C Autoencoder Denoising autoencoder |
| url | https://doi.org/10.1038/s41598-024-59785-y |
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