Cloud Based Framework for Handling Diabetes Data

With the exponential growth of data from various social networks like Facebook, Twitter, Mobile applications, Digital cameras, Sensor networks etc., and also from biomedical researches the overall data volume has increased tremendously. So analysing and extracting fruitful information from such a dynamic data is very much challenging task today. Data mining plays a vital role in handling big data for analysing pattern recognition and medical predictions. We can mine data using various algorithms and techniques such as Classification, Clustering, Regression, Association Rules, etc., These patterns can be utilized for fast and better clinical decision making of preventive and suggestive medicine. It implements an efficient data mining techniques called Frequent Pattern-Growth algorithm (FP-Growth) to analyse the diabetes data set have been collected from various patients and generated useful prediction results. Files stored in the cloud can be accessed at any time from any place so long as you have Internet access. So cloud stores the diabetes data sets and generates useful prediction results using FP-Growth algorithm.

Autorentext

P.Sangeetha pursued her Bachelor's degree in the field of Computer Science and Engineering from SNS College of Technology and M.E degree from KPR Institute of Engineering and Technology, Coimbatore. She is presently working as a Assistant Professor in the department of Computer Science and Engineering at Kathir College of Engineering.