PREDECTING DIABETES MELLITUS ACCURETELY USING MACHINE LEARNING ALGORITHMS IN BIGDATA

The medical industry contains huge amount of data. It is a large in size and very difficult to predict a disease in traditional methods. Diabetics Mellitus is non-communicable diseases. It targets more in middle income countries. As shown by the International Diabetes Federation (IDF), 463 million people have diabetes in the world and 88 million people in the Southeast Asia region. Of these 88 million people, 77 million have a spot with India. The power of diabetes in the general population is 8.9%, as demonstrated by the IDF. As indicated by the IDF gauges, India has the second most noteworthy number of youngsters with type 1 diabetes after the United States. It additionally adds to the biggest extent of occurrence instances of type 1 diabetes in youngsters in the SEA district. Per the World Health Organization, 2% of all passing in India are because of diabetes. As per the developing dreariness as of late, in 2040, the world's diabetic patients will arrive at 642 million, which implies that one of the ten grown-ups later on is experiencing diabetes. There is no uncertainty that this disturbing figure needs incredible consideration. With the quick improvement of Machine learning, Machine Learning has been applied to numerous parts of clinical wellbeing. Diabetes is one of the common and growing diseases in several countries and all of them are working to prevent this disease at early stage by predicting the symptoms of diabetes using several methods we are using Big data analytics to predict the diabetes data accurately. Big data analytics creates awareness about the diabetes among the patients. It helps a patient to resolve and care the diseases with Electronic Health Records (EHR). Based on dataset, big data predict upcoming risk in diabetes and provide a treatment accordingly.in this paper machine learning concepts used to prevent and diagnose the diabetes were discussed and presented an overview of Big Data, Machine learning tools and models.

Keywords: Big Data, Machine Learning, Diabetes.