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The liver function tests (LFTs) remain one of the most commonly employed clinical measures for the diagnosis of hepatobiliary disease. LFTs sometimes referred to as hepatic panel help to determine the health of liver, monitor the progression of a disease and measure the severity of a disease particularly scarring or cirrhosis of the liver.

In this study, we present a new approach to evaluate the natural progression of liver disease through the assessment of eight biochemical parameters: serum total bilirubin (TB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), Alkaline phosphatase (ALP), total protein (TP), albumin (ALB), albumin/globulin (A/G) ratio, and alpha-fetoprotein (AFP) as well as two machine learning (ML) tools—Random Forest and CART to substantive the outcome.

The study was carried out in a total of 100 subjects which included healthy controls (group I-25 patients), patients with acute hepatitis (group II-25 patients), chronic hepatitis (group III-25 patients) and hepatocellular carcinoma (group IV-25 patients) applying both biochemical and Machine Learning methods.

Of the eight parameters tested, all except ALP (p = 0.426), showed an overall discriminatory ability as judged by one-factor analysis of variance (p < 0.0001). We also assessed the differences among group means by least significance difference (LSD). The analysis showed that TB remained significantly elevated in groups II, III, and IV as compared to controls (p < 0.05). ALP did not have any discriminatory power among the four groups tested. ALT and AST were good discriminators only between the control groups …