Prediction of Hepatitis C based on liver function test features

Yuqi Huang; Danni Yang; Xinyi Zhang

doi:10.1117/12.2628030

22 April 2022 Prediction of Hepatitis C based on liver function test features

Yuqi Huang, Danni Yang, Xinyi Zhang

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 121631N (2022) https://doi.org/10.1117/12.2628030
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

Abstract

Hepatitis C is a widespread liver disease that possibly leads to serious symptoms if not diagnosed in time. Currently, several methods are already available for specific screening of Hepatitis C. However, their expensive costs make it hard to allow their broad use in countries with poor conditions. Here, by constructing a mathematical model, we introduce a new method for testing Hepatitis C diagnosis. Our method is based on the results of liver function tests; therefore, it is relatively more cost-saving to do the test. A study was conducted based on the dataset obtained from the UCI Machine Learning Repository at June 10, 2020, containing laboratory values of blood donors and Hepatitis C patients and demographic values like age. χ² and ANOVA test was used to find the correlation between Hepatitis C and parameters of liver function test. Logistics regression was used to build the model for the prediction of Hepatitis C. The result shows that there’s a significant increase in likelihood of Hepatitis C when there’s increase in AST (β = 0.09, p < 0.001) and BIL (β = 0.057, p < 0.01); and there’s also a significant decrease in likelihood of Hepatitis C when there’s increase in ALT (β = -0.026, p < 0.001) and CHOL (β

Citation Download Citation

Yuqi Huang, Danni Yang, and Xinyi Zhang "Prediction of Hepatitis C based on liver function test features", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 121631N (22 April 2022); https://doi.org/10.1117/12.2628030

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