MEDICAL DATA CLASSIFICATION USING DIFFERENT OPTIMIZATION TECHNIQUES: A SURVEY

Abstract

Classification of health data for perfect opinion is a on the rise field of relevance and investigate in records removal. Medical data classification is an important data mining problem being discussed about for a decade that has attracted several researchers around the world. The sorting techniques provide invaluable information to pathologist for diagnosis and treatment of diseases. By detecting and counting blood cell within the blood smear using sorting techniques, it is quite possible to detect so many diseases. The aspiration of projecting data mining in scientific drug is to take models that can use patient exact information sequence, then system will be predict the conclusion of interest and to thereby support scientific administrative. The various data mining algorithms like Classification, Clustering, Regression techniques, soft computing and many more are used in health data sorting. According paper, we have literature survey some of the methods correlated to health data sorting. The chief heart of survey is to scrutinize DMT like ANN, decision tree, support vector machine, nearest neighbor, Bayesian algorithm required for health statistics removal above all to determine nearby regular diseases such as swine flu, heart disease, lung tumor, dengue, breast growth tumor and so on.