Rule Optimization of Boosted C5.0 Classification Using Genetic Algorithm for Liver disease Prediction

One of the interesting and important subjects among researchers in the field of medical and computer science is diagnosing illness by considering the features that have the most impact on recognitions. The subject discusses a new concept which is called Medical Data Mining (MDM). Indeed, data mining methods use different ways such as classification and clustering to classify diseases and their symptoms which are helpful for diagnosing. This paper introduces a new method of liver disease diagnosis to help doctors and their patients in finding the disease symptoms and reduce a long time of diagnosing and prevent deaths. The proposed method will optimize the rules released from Boosted C5.0 classification method with the Genetic Algorithm (GA), to increase the diagnosis time and accuracy. So instead of using an evolutionary algorithm for producing rules, the genetic algorithm is used for improving and reducing rules of another algorithm. We show that our proposed approach has better performance and throughput in comparison with other work in the field. The accuracy is improved from 81% in [1] to 93% in our work.