Vol. 1, Issue 1, Part A (2024)

Liver diseases are a major global health concern, with conditions such as hepatitis, cirrhosis, and liver cancer causing significant morbidity and mortality. Hepatic enzymes, such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), and alkaline phosphatase (ALP), are key biomarkers in the diagnosis and monitoring of liver dysfunction. Traditional diagnostic methods, however, are often limited by their dependence on invasive procedures and slow results. In recent years, machine learning (ML) techniques have gained prominence in medical diagnostics, offering the potential for early detection of liver disorders through predictive modeling of hepatic enzyme activity. This review explores the application of ML models in predicting liver enzyme levels, focusing on their accuracy, efficiency, and potential in clinical practice. We discuss various machine learning algorithms, including decision trees, support vector machines, and neural networks, and their ability to process large datasets derived from clinical, demographic, and biochemical parameters. The challenges in model interpretability, data quality, and clinical integration are also highlighted. By leveraging patient data, these models can assist in the early diagnosis of liver conditions, allowing for timely intervention and improved patient outcomes. This review aims to assess the current state of ML-based predictive models for hepatic enzyme activity, evaluate their clinical applicability, and propose future directions for integrating these technologies into routine medical practice.