Prediction of Diabetes Risk based on Machine Learning Techniques

The explosive population growth and health maintenance is an extremely crucial matter worldwide. Many lethal diseases are causing threats at a high peak in recent years. Introducing machine learning technologies into healthcare for early prognosis and diagnosis need to be more accurate based on the parameters and frames selected from the available clinical databases. The objective of this paper is to analyze, explore various research outcomes of machine learning methodologies used in diabetes mellitus and how the efficiencies obtained could be helpful in future perspective of a predictive diabetes model designing. The exploration inferred that more variables and hybrid disciplines should be considered for an accuracy of result which can overcome the existing limitations.