Supervised Machine Learning Models for Liver Disease Risk Prediction
Ηλίας Δρίτσας, Μαρία Τρίγκα
Abstract
The liver constitutes the largest gland in the human body and performs many different functions. It processes what a person eats and drinks and converts food into nutrients that need to be absorbed by the body. In addition, it filters out harmful substances from the blood and helps tackle infections. Exposure to viruses or dangerous chemicals can damage the liver. When this organ is damaged, liver disease can develop. Liver disease refers to any condition that causes damage to the liver and may affect its function. It is a serious condition that threatens human life and requires urgent medical attention. Early prediction of the disease using machine learning (ML) techniques will be the point of interest in this study. Specifically, in the content of this research work, various ML models and Ensemble methods were evaluated and compared in terms of Accuracy, Precision, Recall, F-measure and area under the curve (AUC) in order to predict liver disease occurrence. The experimental results showed that the Voting classifier outperforms the other models with an accuracy, recall, and F-measure of 80.1%, a precision of 80.4%, and an AUC equal to 88.4% after SMOTE with 10-fold cross-validation.