Liver disorder diagnosis using linear, nonlinear and decision tree classification algorithms

In India and across the globe, liver disease is a serious area of concern in medicine. Therefore, it becomes essential to use classification algorithms for assessing the disease in order to improve the efficiency of medical diagnosis which eventually leads to appropriate and timely treatment. The study accordingly implemented various classification algorithms including linear discriminant analysis (LDA), diagonal linear discriminant analysis (DLDA), quadratic discriminant analysis (QDA), diagonal quadratic discriminant analysis (DQDA), naive bayes (NB), feed-forward neural network (FFNN) and classification and regression tree (CART) in an attempt to enhance the diagnostic accuracy of liver disorder and to reduce the inefficiencies caused by false diagnosis. The results demonstrated that CART had emerged as the best model by achieving higher diagnostic accuracy than LDA, DLDA, QDA, DQDA, NB and FFNN. FFNN stood second in comparison and performed better than rest of the classifiers. After evaluation, it can be said that the precision of a classification algorithm depends on the type and features of a dataset. For the given dataset, decision tree classifier CART outperforms all other linear and nonlinear classifiers. It also showed the capability of assisting clinicians in determining the existence of liver disorder, in attaining better diagnosis and in avoiding delay in treatment. Keyword-Liver disease diagnosis; classification algorithms; classification and regression tree; linear discriminant analysis; quadratic discriminant analysis; naïve bayes classifier; feed-forward neural network; computational biology.