Analysis of Liver Disorder by Machine Learning Techniques

In the current scenario, the classification methods are needed to reduce the possible errors. It will help the physicians to take suitable decisions with speedy manner. Here, the prime motto of this paper is to achieve an efficient classification method for liver disease. So, authors have used random forest, support vector machine and AdaBoost methods on the Indian Liver Patient Disease (ILPD) data set where random forest gives the highest accuracy of 93%. Finally, authors would like to conclude that the proposed classification methods have not improved but sustained the accuracy compared to the existing and could also be implemented in other medical diseases.