An empirical comparative analysis using machine learning techniques for liver disease prediction

Abstract

The human liver is one of the major organs in the body, and liver disease can cause many problems in human lives. Due to the increase in liver disease, various data mining techniques are proposed by the researchers to predict liver disease. These techniques are improving day by day in order to predict and diagnose liver disease in humans. In this paper, a real-world liver disease dataset is incorporated for diagnosing liver disease in the human body. For this purpose, feature selection models are used to select a number of features that are the most important features to diagnose liver disease. After selecting features and splitting data for training and testing, different classification algorithms in terms of naïve Bayes, supervised vector machine, decision tree, k-nearest neighbor, and logistic regression models to diagnose liver disease in human body are explored. The results are cross validated by tenfold cross-validation methods and achieve an accuracy as good as 93%.