Usage of Weka Software Based on Machine Learning Algorithms for Prediction of Liver Fibrosis/Cirrhosis

Abstract

The liver, a life-sustaining organ, plays a substantial role in many body functions. Liver diseases have become an important world health problem in terms of prevalence, incidences, and mortalities. Liver fibrosis/cirrhosis is great of importance, because if not treated in time liver cancer could be occurred and spread to other parts of the body. For this reason, early diagnosis of liver fibrosis/cirrhosis gives significance. Accordingly, this study investigated the performances of different machine learning algorithms for prediction of liver fibrosis/cirrhosis based on demographic and blood values. In this context, random forest, k nearest neighbour and C4.5 decision tree algorithms were used and these algorithms were implemented on WEKA data mining tool. The obtained results revealed out that random forest algorithm outperformed in term of all evaluation metrics (90.91% accuracy, 90% specificity, 90% precision, 91.8% recall, 0.909 F-measure and 0.962 ROC) as compared with other algorithms.