Diabetes prediction in healthcare systems using machine learning algorithms on Hadoop cluster

Health care systems are merely designed to meet the needs of increasing population globally. People around the globe are affected with different types of deadliest diseases. Among the different types of commonly existing diseases, diabetes is a major cause of blindness, kidney failure, heart attacks, etc. Health care monitoring systems for different diseases and symptoms are available all around the world. The rapid development in the fields of Information and Communication Technologies made remarkable improvements in health care systems. Various Machine Learning algorithms are proposed which automates the working model of health care systems and enhances the accuracy of disease prediction. Hadoop cluster based distributed computing framework supports in efficient processing and storing of extremely large datasets in cloud environment. This work proposes the novel implementation of machine learning algorithms in hadoop based clusters for diabetes prediction. The results show that the machine learning algorithms can able to produce highly accurate diabetes predictive healthcare systems. Pima Indians Diabetes Database from National Institute of Diabetes and Digestive Diseases is used to evaluate the working of algorithm.