Performance Analysis of Machine Learning Algorithms for Prediction of Liver Disease

Liver diseases like fatty liver disease, chronic active hepatitis, and cirrhosis are the major cause of mortality in India. Alcohol consumption, inhalation of harmful toxic gases, improper consumption of contaminated pickles, drugs, and foods are the major cause of diseases in the liver. Diagnosis of liver disease needs high accuracy and precise results for predicting whether a person is suffering from liver disease or not. Major disastrous repercussions can be the result of minor errors in the diagnosis of liver diseases. The major goal of this paper is for the detection of liver disease at right time and helping the doctors and combating the increasing number of cases. In this paper, we implemented various machine learning techniques like logistic regression, KNN, XG-Boost, SVM, Gaussian NB, Random forest, Decision tree, Gradient Boosting, CatBoost, AdaBoost, and LightGBM on selected features from the dataset for predicting liver disease and it was found that Random Forest performed best among all the technique and gained high accuracy and performed outstandingly in all metric evaluations.