The comparison of machine learning classification algorithms used to diagnose liver cirrhosis disease and a brief review

Abstract

Liver cirrhosis disease is an important cause of death worldwide. Therefore, early diagnosis of the disease is very important. Machine learning algorithms are frequently used due to its high performance in the field of health, as in many areas. In this study, Multilayer Perceptron-Artificial Neural Networks, Decision Trees, Random Forest, Naïve Bayes, Support Vector Machines, K-Nearest Neighborhood, and Logistic Regression classification algorithms are used to classify the factors affecting liver cirrhosis. The performances of these algorithms are compared according to the accuracy rate, F measure, sensitivity, specificity and Kappa score on real data obtained from 2000 liver cirrhosis patients, and the factors affecting the disease are classified with the most appropriate algorithm. In addition, more than 50 articles covering both liver disease and classification methods are reviewed and the latest developments are presented in the study.