Diagnosing liver diseases with machine learning

The methods and applications of operational intelligence are among the methods which are used frequently for medical diagnosis with remarkable success. Within this context, it is believed that this study on the diagnosis of various liver diseases using operational intelligence methods together with the developed interface will have a positive effect on the decision making process of physicians. In this study a large comprehensive database of fourteen different liver diseases is formed. Testing "Decision-Tree Algorithms" as one of the operational intelligence methods, the software has been developed in a way to guarantee best results. While developing the software seven different categories are determined for seventy-one data groups of tests and physical examination used for diagnosis. These seven categories have been determined according to the level of difficulty, accuracy and the cost of the tests and physical examinations. With the help of the developed interface it is aimed for the specialized physician to get probable results at any stage of the diagnosis by entering the data s/he has gathered.