Clinical Usefulness of Machine Learning Approaches as a Non-Invasive Technology in Reducing Hepatitis Disease Mortality

Hepatitis is a viral infection that causes inflammation of the liver. However, other factors can cause the disease, including secondary effects from drugs, toxins, alcohol, and autoimmune hepatitis. The autoimmune form of the disease occurs when the body produces antibodies against the liver tissue, and many people worldwide are affected by it. Various clinical factors and parameters are examined in diagnosing hepatitis disease, which is analyzed by performing various tests of these factors and parameters. As a result of the vastness of the parameters under examination, it is challenging and complicated for the experts in this field to perform the analysis process on these parameters on a large scale. Healthcare experts can now identify the factors influencing the death rate of patients with high speed and accuracy thanks to emerging technologies such as machine learning, which is a subset of artificial intelligence. In this study, KNN and SVM machine learning techniques were used to analyze the positive effect of clinical parameters such as LIVER BIG, LIVER FIRM, SPLEEN PALPABLE, and ANOREXIA on patients' survival or death rates. This study investigates and analyzes the results of