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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 infection (accuracy = 85.4%, sensitivity = 91%, specificity = 72.6%, precision = 88.2%, F1-score = 0.89, Area Under the Receiver Operating Curve, AUC = 0.90) proved to be highly accurate for discriminating HBsAg positive from negative patients, and thus rivals with immunoassay. Therefore, we propose a predictive model based on routine blood tests as a novel diagnostic for early detection of HBV infection. Early prediction of HBV infection via routine pathology markers and pattern recognition algorithms will offer decision-support to clinicians and enhance early diagnosis, which is critical for optimal clinical management and improved patient outcomes.
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Details
1 ANU College of Health and Medicine, The Australian National University, National Centre for Epidemiology and Population Health, Acton, Australian Capital Territory, Australia (GRID:grid.1001.0) (ISNI:0000 0001 2180 7477); Kwara State University, Department of Microbiology, Faculty of Pure and Applied Sciences, Malete, Nigeria (GRID:grid.442596.8) (ISNI:0000 0004 0461 8297)
2 The Australian National University, Statistical Support Network, Acton, Australian Capital Territory, Australia (GRID:grid.1001.0) (ISNI:0000 0001 2180 7477)
3 ANU College of Health and Medicine, The Australian National University, National Centre for Epidemiology and Population Health, Acton, Australian Capital Territory, Australia (GRID:grid.1001.0) (ISNI:0000 0001 2180 7477)
4 The University of Sydney, Sydney School of Public Health, Faculty of Medicine and Health, New South Wales, Australia (GRID:grid.1013.3) (ISNI:0000 0004 1936 834X)
5 The Nigerian Institute of Medical Research, Microbiology Department, Centre for Human Virology and Genomics, Yaba, Nigeria (GRID:grid.416197.c) (ISNI:0000 0001 0247 1197)
6 The Nigerian Institute of Medical Research, Director-General’s Office, Yaba, Nigeria (GRID:grid.416197.c) (ISNI:0000 0001 0247 1197)
7 University of Ilorin Teaching Hospital, Department of Medicine, Ilorin, Nigeria (GRID:grid.412975.c) (ISNI:0000 0000 8878 5287)