Experimental Analysis of Available Healthcare Dataset Using Various Classification Algorithm

Abstract

Dynamic data classification is the most vital task in data mining as well as machine learning algorithms. Many existing algorithms have been proposed with different classification techniques to cluster the data according to Background Knowledge (BK). In supervised learning, various machine learning algorithms determine preeminent classification accuracy for synthetic as well as real time dataset. To process a large amount of data in a parallel manner using distributed environment using machine learning has been carried out in many existing systems. The proposed research describes dynamic data classification using Fuzzy Random Forest (FRF) in distributed environment. It also carried out privacy approaches during data distribution which overcomes to leakage issues in vulnerable environments. Four different types of data set have been used for experimental analysis, including synthetic as well as real time instance. In the first phase system deals with fuzzy random forest to generate the background knowledge of system. Similarly, it also extracts training dataset information from clinicaltrials.gov. Disease information has been provided like symptoms, treatment, prevention, age and gender respectively. Once the train model has been built, it shows all the rules in the train database. newline