Predictive Modeling Framework for Diabetes Classification Using Big Data Tools and Machine Learning

Abstract

Diabetics is now a days a common and most threatening disease irrespective of age, gender and becoming a human threat. As the Internet of Things (IoT) environment is growing rapidly in health sector and continuously gathering the data from smart health care which directly reflects the growth of the big data. Predictive modeling helps doctors and physicians to the identify the growth of diabetics in patients from early ages and create an alarm such to make the patient more attentive towards the diabetics. Based on the previous approaches on diabetics prediction over big data related diabetic prediction yields in better understanding from the patient perspective. The approach for the proposed system is much wider in term of predicting the diabetic model with enough feature variables explaining the patient historical data and diet habits. The Frame work has been carried based on extensive machine learning methods in association for processing of the data over spark RDD. The Random Forest and Ada Boost algorithm showed us a prominent values in terms of predicting the results.