Review of Medical Disease Symptoms Prediction Using Data Mining Technique

Abstract

Now a day's data mining technique used in the field of medical diagnose of critical diesis and clinical data. The prediction of mining technique is major issue. For the enhancement of mining technique used various approach such as fuzzy logic, feature optimization and machine learning based classification technique. in this classification proceed based on classifier selection to medical disease data and propose a clustering-based classifier selection method. In the method, many clusters are selected for an ensemble process. Then, the standard presentation of each classifier on selected clusters is calculated and the classifier with the best average performance is chosen to classify the given data. In the computation of normal act, weighted average is technique is used. Weight values are calculated according to the distances between the given data and each selected cluster. There are generally two types of multiple classifiers combination: multiple classifiers selection and multiple classifiers fusion. Multiple classifiers selection assumes that each classifier has expertise in some local regions of the feature space and attempts to find which classifier has the highest local accuracy near an unknown test sample. Then, this classifier is nominated to make the final decision of the system.