A Comparative Study on the Performance of Classification Algorithms for Effective Diagnosis of Liver Diseases

Abstract

In recent years, different approaches and methods have been proposed to diagnose various diseases accurately.Since there are a variety of liver diseases, till late-stage liver disease and liver failure occur the symptoms tend tobe specific for that illness. Therefore, early diagnosis can play a key role in preventing deaths from liver diseases.In this study, we compare the accuracy of different classification methods supported by the SAS software suite,such as Neural Network, Auto Neural, High Performance (HP) SVM, HP Forest, HP Tree (Decision Tree), andHP Neural for the diagnosis of liver diseases. In this study, the Indian Liver Patient Dataset (ILPD) provided bythe University of California, Irvine (UCI) repository is used. Experimental results show that based on the metricsof our study, in the training phase while HP Forest achieves the highest accuracy rate, HP SVM and HP Tree dothe lowest accuracy rates. However, in the validation phase, Neural Network achieves the highest accuracy rateand HP Forest does the lowest accuracy rate. Our experimental results may be useful for both researchers andpractitioners working in related fields.