The development of a machine learning algorithm for early detection of viral hepatitis B infection in Nigerian patients.

ABSTRACT: Access to Hepatitis B Virus (HBV) testing for people in low-resource settings has long been challenging due to the gold standard, enzyme immunoassay, being prohibitively expensive, and requiring specialised skills and facilities that are not readily available, particularly in remote and isolated laboratories. Routine pathology data in tandem with cutting-edge machine learning shows promising diagnostic potential. In this study, recursive partitioning ("trees") and Support Vector Machines (SVMs) were applied to interrogate patient dataset (n = 916) that comprised results for Hepatitis B Surface Antigen (HBsAg) and routine clinical chemistry and haematology blood tests. These algorithms were used to develop a predictive diagnostic model of HBV infection. Our SVM-based diagnostic model of inf

SUBMITTER: Ajuwon BI 

PROVIDER: S-EPMC9958122 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature