Classification with Feature Selection of Medical Data: A Review

Medical data is all about to help spot trends recommend resources and help corresponding patient. It helps to doctors to understand the situation of the patient. In this paper, Feature Selection (FS) methods to deal with medical data by using different methods such as Receiver Operating Characteristic (ROC), Sequential Forward Floating Search (SFFS), Classification and Regression Tree (CART), and Particle Swarm Optimization (PSO). ROC as well as SFFS methods help to reduce over-appropriate issue and diminish the probabilities of receiving a confined optimal result. With PSO, filter and wrapper methods help to enhance the classification accuracy. And, CART method is used with the Boruta method to reduce the dimension of dataset (UCI Dataset).