Prediction of Liver Disease Using Machine Learning Algorithms

Most important internal organ of human body is liver, and its primary functions are to break down food, to maintain energy minerals and vitamins and to get rid of waste that is generated by our organs. Damage of liver can cause numerous death dealing disorders like liver cancer, so we must treat the patients to lower the probability of those fatal diseases in early stage. Curing of liver disease is exorbitant and advanced, and various analyses have been carried out by making use of machine learning (ML) procedures for analyzing liver disease cases. In this research, we have used five different ML algorithms like k-nearest neighbor (KNN), decision tree (DT), extra trees (ET), logistic regression (LR) and random forest (RF), to examine the Indian Liver Patient Dataset (ILPD). For qualified analysis, we have measured a F1-score, accuracy, precision, specificity, ROC (receiver operating characteristic curve) and sensitivity. After detailed differentiation of results, we noticed that ET gives the maximum accuracy of 92.5%.