www.astesj.com 1618 Elasticity Based Med-Cloud Recommendation System for Diabetic Prediction in Cloud Computing Environment Karamath Ateeq, Manas Ranjan Pradhan * , Beenu Mago School of Information Technology, Skyline University College, Sharjah, UAE A R T I C L E I N F O A B S T R A C T Article history: Received: 14 August, 2020 Accepted: 05 November, 2020 Online: 25 December, 2020 Day to day huge medical data have been accumulating for diabetic diseases. The complexity of storing, processing ,analyzing and predicting the data related to diabetics is not so easy for healthcare professionals .The prediction of accurate results also has the limitation due to scale of data increasing worldwide for patients, symptoms and test results .In this paper ,it has been tried to considered the diabetic related data storage on cloud and adopt integrated computational algorithms of datamining for better prediction system to various diabetic types(Type 1, Type 2 and Gestational).Though many computational prediction model and recommended system have been proposed by many researchers ,the proposed model has the novelty of considering the elasticity in data analysis due to frequent data changes of patients due to diabetic test time to time. In this work, Elasticity based Med-Cloud Recommendation System (EMCRS) is proposed for predicting the diabetic disease types and providing recommendations for the patients diagnosed with diabetes. Moreover, elastic resource allocation mechanism is proposed to provide cloud resources an on-demand basis to EMCRS.Various computational algorithms have been used for different proposed to make EMCRS to predict results as compared other existing system. The Adaptively Toggle Genetic Algorithm (ATGA) is applied for elastic resource allocation while increase in the number of data sets. ATGA has taken toggle genetic algorithm that shifts between Roulette Wheel Selection Operator. Hybrid Classification and Clustering Algorithm (HC2A) is used for classifying and clustering the diseased patients as Type 1, Type 2 and Gestational Diabetic patients. Fuzzy C Means clustering based attribute weighting (FCMAW) was used for classifying the diabetic data set. The accuracy of the system tested on Pima Indian Diabetic Dataset (PID) and US Diabetic Dataset (USD) from UCI website which is approximately 98% classification accuracy. Keywords: Diabetes Data Mining Cloud Computing Medical Cloud Prediction Fuzzy Clustering Neural Network Genetic Algorithm Recommendation System 1. Introduction Diabetes is a chronic disease that begins with the failure of pancreas. The pancreas fails to produce sufficient insulin required by the body [1]. The internal changes prompt to an increased concentration of glucose in the blood. It is a condition of high blood glucose level in diabetic patients. It can cause either Type I or Type II diabetes. Type I is known as insulin dependent diabetes, which occurs when there is lack of insulin production. Type II diabetes is non-insulin dependent which is caused by the ineffective use of insulin by human body. This will result in excess body weight and physical inactivity. An earlier prediction or recommendation system is needed to save the patients from the risk of diabetes. In such a condition, Data Mining is suggested and found to be a better diagnostic tool which can be used by the medical practioners too. Data Mining is the process of selecting, exploring and modeling large amounts of data [2], [3]. This process has become an increasingly pervasive activity in all areas of medical science research. Data mining has resulted in the discovery of useful hidden patterns from massive databases. Consequently, data mining tools are now being used for clinical data. The bottle neck in data analysis is now raising the most appropriate clinical questions and using proper data and analysis techniques to obtain ASTESJ ISSN: 2415-6698 * Corresponding Author: Manas Ranjan Pradhan, School of IT, Skyline University College, Sharjah, UAE, manas.pradhan@skylineuniversity.ac.ae Advances in Science, Technology and Engineering Systems Journal Vol. 5, No. 6, 1618-1633 (2020) www.astesj.com https://dx.doi.org/10.25046/aj0506193