Abstract
Every year, around 1.5 million of the world population succumbs to the Hepatitis C Virus. 70% of these cases develop chronic infection and cirrhosis within the next 20 years. Because there is no effective treatment for HCV, it is critical to predicting the virus in its early stages. The study’s goal is to define a data-driven approach for accurately detecting HCV severity in patients. Our approach achieves the highest accuracy of 86.79% compared to 70.89% using the standard approach.
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Sharma, A., Arora, A., Gupta, A., Singh, P.K. (2022). Data-Centric Approach to Hepatitis C Virus Severity Prediction. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_39
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