A Comparative Analysis of Machine Learning Algorithms to Predict Liver Disease

The liver is considered an essential organ in the human body. Liver disorders have risen globally at an unprecedented pace due to unhealthy lifestyles and excessive alcohol consumption. Chronic liver disease is one of the principal causes of death affecting large portions of the global population. An accumulation of liver-damaging factors deteriorates this condition. Obesity, an undiagnosed hepatitis infection, alcohol abuse, coughing or vomiting blood, kidney or hepatic failure, jaundice, liver encephalopathy, and many more disorders are responsible for it. Thus, immediate intervention is needed to diagnose the ailment before it is too late. Therefore, this work aims to evaluate several machine learning algorithm outputs, namely logistic regression, random forest, XGBoost, support vector machine (SVM), AdaBoost, K-NN, and decision tree for predicting and diagnosing chronic liver disease. The classification algorithms are evaluated based on various measurement criteria, such as accuracy, precision, recall, F1 score, an area under the curve (AUC), and specificity. Among the algorithms, the random forest algorithm showed better performance in liver disease prediction with an accuracy of 83.70%. Furthermore, the random forest algorithm also showed better precision, F1, recall, and AUC metrics. Hence, random forest is considered the best algorithm for early liver disease prediction.