High-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis
Abstract Chronic HBV infection, the main cause of liver cirrhosis and hepatocellular carcinoma, has become a global health concern. Machine learning algorithms are particularly adept at analyzing medical phenomenon by capturing complex and nonlinear relationships in clinical data. Our study proposed...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2021-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-84556-4 |
| Summary: | Abstract Chronic HBV infection, the main cause of liver cirrhosis and hepatocellular carcinoma, has become a global health concern. Machine learning algorithms are particularly adept at analyzing medical phenomenon by capturing complex and nonlinear relationships in clinical data. Our study proposed a predictive model on the basis of 55 routine laboratory and clinical parameters by machine learning algorithms as a novel non-invasive method for liver fibrosis diagnosis. The model was further evaluated on the accuracy and rationality and proved to be highly accurate and efficient for the prediction of HBV-related fibrosis. In conclusion, we suggested a potential combination of high-dimensional clinical data and machine learning predictive algorithms for the liver fibrosis diagnosis. |
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| ISSN: | 2045-2322 |