Hepatitis C Virus positivity prediction from serum samples using NIRS and L1-penalized classification.

Aims: The hepatitis C virus (HCV) has developed a strategy to coexist with its host resulting in varying degrees of tissue and cell damage, which generate different pathological phenotypes, such as varying degrees of fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). However, there is no integrated information that can predict the evolutionary course of the infection. We propose to combine Near-infrared spectroscopy (NIRS) and machine learning techniques to provide a predictive model.