Analysis Machine Learning Methods For Forecasting Liver Disease

The majority of people worldwide are affected by chronic liver disease, which is the leading cause of death worldwide. It is now extremely challenging for researchers in the healthcare industry to predict diseases from the extensive databases. We use classification algorithms from machine learning to resolve this issue. Predicting liver disease is the primary objective of this project. We have implemented five machine learning algorithms: Naive Bayes, SVM, K-Nearest Neighbor, Logistic Regression, and Random Forest. The comparison of these classifier algorithms is entirely based on performance, classification accuracy and execution time. As a result, the objective of our project is to compare and contrast the overall performance of various machine learning algorithms in order to lessen the exorbitant cost of liver disease prediction