Data Mining Approaches for Diabetes using Feature selection

Abstract

Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Data mining is applied to find patterns to help in the important tasks of medical diagnosis and treatment. This project aims for mining the diabetes types with the help of feature selection techniques. The main interest is to identify research goals. Data mining has played an important role in diabetes research. Data mining would be a valuable asset for diabetes researchers because it can unearth hidden knowledge from a huge amount of diabetes-related data. The results could be used for both scientific research and real-life practice to improve the quality of health care diabetes patients. This article describes applications of data mining for the analysis of blood glucose and diabetes mellitus data.

The main purpose of this paper is to predict how people with different age groups are being affected by diabetes based on their life style activities and to find out factors responsible for the individual to be diabetic. The Best First Search Technique is implemented in this approach to fetch the data from the database. This approach is used to retrieve data in efficient manner and also in fraction of seconds without any delay. The greedy forward selection method also implemented in this approach to update the data in the database