Diseases are increasing rapidly now a days due to number of reasons. It will be very helpful to cure that disease if we predict occurrences of diseases in the early stages. Even though doctors and health centers collect data daily but most of them are not using machine learning and pattern matching techniques to extract the knowledge that can be very useful in prediction. We have chosen dataset of liver diseases to evaluate prediction algorithms in an effort to reduce burden on doctors. In our work, we have trained eight models Logistic Regression, Random Forest, XGBoost, KNN, Decision Trees, SVC, Gradient Boosting and Neural Network. The analysis compare all these models and choose the best model.

Data Mining, Classification, Decision Tree, Liver Disease

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