Identifying efficient features in diagnose of liver disease by decision tree models

Background: liver is one of the vital organs of human body and its health is of utmost importance for our survival. Automatic classification instruments as a diagnostic tool help to reduce the working load of doctors. But the most important thing is to identify the factors in diagnosis. Choosing the wrong diagnosis of the factors affecting the system classify error detection and to be complex. The purpose of this research is to identify efficient features in diagnose of liver disease. Intelligent diagnosis models used in this research are QUEST, C5.0, CRT and CHAID. Materials and Methods: Data were collected from the records of 583 patients in the North East of Andhra Pradesh, India. Data were all registered at the University of California in 2012. Four tree models were compared by the specificity, sensitivity, accuracy and area under ROC curve. Results: The accuracy of the classification four models; QUEST, C5.0, CRT and CHAID, with INN classifier were 70%, 71%, 66% and 64% and with Nive Bayes classifier were 55%,54%,50% and 55% respectively. Conclusion: CHAID and QUEST models were considered as the best model with the highest precision. Therefore; CHAID and QUEST models are proposed to identify efficient features in diagnose of the liver disease. This paper is invaluable in terms of research activities in the field of health and it is especially important in the allocation of health resources for risky people.

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