Predictive analysis of diabetic patient data using machine learning and Hadoop

Now days from health care industries large volume of data is generating. It is necessary to collect, store and process this data to discover knowledge from it and utilize it to take significant decisions. Diabetic Mellitus (DM) is from the Non Communicable Diseases (NCD), and lots of people are suffering from it. Now days, for developing countries such as India, DM has become a big health issue. The DM is one of the critical diseases which has long term complications associated with it and also follows with various health problems. With the help of technology, it is necessary to build a system that store and analyze the diabetic data and predict possible risks accordingly. Predictive analysis is a method that integrates various data mining techniques, machine learning algorithms and statistics that use current and past data sets to gain insight and predict future risks. In this work machine learning algorithm in Hadoop MapReduce environment are implemented for Pima Indian diabetes data set to find out missing values in it and to discover patterns from it. This work will be able to predict types of diabetes are widespread, related future risks and according to the risk level of patient the type of treatment can be provided.