Performance Evaluation of Random Forests and Artificial Neural Networks for the Classification of Liver Disorder

Abstract

Liver is the major organ inside the human body which is very supportive for digesting food, eliminating poisons, and stocking energy. The rate of Liver disorder patients is rapidly rising all over the world. But it is very hard to identify the disorder from its ambiguous symptoms which increases the mortality rate due to this disease. The paper represents an expert scheme for the classification of liver disorder using Random Forests (RFs) and Artificial Neural Networks (ANNs). The methods train the input features using 10-fold cross validation fashion. The dataset named as BUPA liver dataset is retrieved from UCI machine learning repository for our research study. The performance of the proposed scheme is assessed in view of accuracy, positive predictive value, negative predictive value, sensitivity, specificity and F1 score. The scheme delivers a better result for training but comparatively low for testing. The scheme obtained the accuracy of 80% and 85.29% by RFs and ANNs respectively along with the F1 score of 75.86% and 82.76% in testing phase.