Prediction of Liver Disorders using Machine Learning Algorithms: A Comparative Study

Liver, a crucial interior organ of the human body whose principal tasks are to eliminate generated waste produced by our organism, digest food, and preserve vitamins and energy materials. The liver disorder can cause various fatal diseases, including liver cancer. Early diagnosis, and treating the patients are compulsory to reduce the risk of those lethal diseases. As the diagnosis of liver disease is expensive and sophisticated, numerous researches have been performed using Machine Learning (ML) methods for classifying liver disorder cases. In this paper, we have compared four different ML algorithms such as Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Extra Trees (ET) for classifying Indian Liver Patient Dataset (ILPD). Pearson Correlation Coefficient based feature selection (PCC-FS) is applied to eliminate irrelevant features from the dataset. Also, a boosting algorithm (AdaBoost) is utilized to enhance the predictive performance of those algorithms. The comparative analysis is evaluated in terms of accuracy, ROC, F-1 score, precision, and recall. After comparing experimental results, we have found that boosting on ET provides the highest accuracy of 92.19%.