Prediction of Liver Diseases Using Artificial  Intelligence: A comparative study of various machine learning models

liver disease is a chronic condition which results in serious health conditions if it is not diagnosed and treated appropriately. The early detection of liver disease is important in order to prevent the onset of severe consequences such as hepatic failure, cirrhosis, and hepatocellular carcinoma. Machine learning, a subfield of artificial intelligence, has the potential to analyze patterns in patient data and forecast the probability of liver disease. The objective of this study is to construct and evaluate accurate and precise diagnostic models for liver disease by utilizing various machine learning techniques, including Random Forest, Logistic Regression, Support Vector Classification, K-Nearest Neighbors, Decision Tree, and XGBoost. Through the utilization of machine learning models on the selected dataset, our aim is to make a valuable contribution to the continuous endeavors aimed at enhancing the identification and management of liver disease, with a particular focus on regions where their incidence is high.