A support vector machine approach to assess drug efficacy of interferon-alpha and ribavirin combination therapy

Background: Interferon-or. (IFN alpha) in combination with ribavirin can be used for the treatment of patients with chronic hepatitis C. This therapeutic approach achieves an overall sustained response rate of approximately 40%, but treatment takes 6-12 months and patients often experience significant adverse reactions. Objective: We aim to develop a tool to distinguish potential responders from nonresponders prior to initiation of IFN alpha-ribavirin treatment. Methods: Using single nucleotide polymorphisms (SNPs) and viral genotype, we applied the Support vector machine (SVM) algorithm 10 build a tool to predict responsiveness to IFN alpha-ribavirin combination therapy. Furthermore, We utilized the SVM algorithm with the recursive feature elimination method to identify a Subset of factors that are significantly more influential than the others. Results and conclusion: The SVM model is a promising method For interring responsiveness to IFN alpha. dealing with the complex nonlinear relationship between factors (Such as SNPs and viral genotypic) and Successful therapy. In this Study, we demonstrate that Our tool may allow patients and doctors to make more informed decisions by analyzing host SNP and viral genotype information.