Liver Disease Prediction Model Using SVM and Logistic Regression

Because of causes including excessive alcohol use, breathing toxic fumes, and ingesting tainted food, pickles, and medications, liver illnesses are growing more and more dangerous in many nations. To help alleviate the burden on doctors, liver patient datasets have been analyzed to develop classification models that can predict liver disease. A patient's liver is completely examined using machine learning algorithms to look for a chronic liver condition that lasts longer than six months. When a training dataset is provided, various classification strategies have been proposed to enhance classification performance. In this study, SVM and Logistic Regression are proposed as machine learning algorithms for identifying liver disease. Several performance measures were used to assess the performance of each model.