Machine Learning Algorithms for Binary Classification of Liver Disease
Anton Sokoliuk, Galyna Kondratenko, Ievgen Sidenko, Yuriy Kondratenko, Anatoly Khomchenko, Igor Atamanyuk
Abstract
The number of patients with liver diseases has been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles, and drugs. Early diagnosis of liver problems will increase patients' survival rates. Liver disease can be diagnosed by analyzing the levels of enzymes in the blood. Creating automatic classification tools may reduce the burden on doctors. To achieve this numerous classification algorithm (Decision Tree, Random Forest, SVM, Neural Net, Naive Bayes, and others) from different machine learning libraries (Scikit-learn, ML.Net, Keras) are tested against existing liver patients' dataset, considering appropriate for each algorithm preliminary data processing. These algorithms evaluated based on three criteria: accuracy, sensitivity, specificity.