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• W3134405738 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." @default.
• W3134405738 created "2021-03-15" @default.
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• W3134405738 date "2021-03-03" @default.
• W3134405738 modified "2023-09-24" @default.
• W3134405738 title "High-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis" @default.
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• W3134405738 doi "https://doi.org/10.1038/s41598-021-84556-4" @default.
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