Comparison of the Decision Tree Models to Intelligent Diagnosis of Liver Disease

Background: \nLiver is one of the vital organs of human body and its health is of utmost importance for our survival. \nAutomatic classification instruments, as a diagnostic tool, help to reduce the working load of doctors. But \nthe concern is that, liver diseases are not easily diagnosed and there are many causes and factors related to \nthem. The purpose of this research is to compare the decision tree models to intelligent diagnosis of liver \ndisease. Intelligent diagnosis models used in this research are QUEST, C5.0, CRT and CHAID. \nMaterial and Methods: \nData were collected from the records of 583 patients in the North East of Andhra Pradesh, India. Four tree \nmodels were compared by the specificity, sensitivity, accuracy, and area under ROC curve. \nResults: \nThe accuracy of the classification tree models; QUEST, C5.0, CRT, and CHAID were 73%, 71%, 75%, and \n86% respectively. \nConclusion: \nCHAID model was considered as the best model with the highest precision. Therefore; CHAID model can \nbe proposed in the diagnosis of the liver disease. This paper is invaluable in terms of research activities in \nthe field of health and it is especially important in the allocation of health resources for risky people.