  Type 2 Diabetes is a fast-growing, chronic metabolic disorder due to
imbalanced insulin activity.The motion of this research is a comparative study
of seven machine learning classifiers and an artificial neural network method
to prognosticate the detection and treatment of diabetes with high accuracy,in
order to identify and treat diabetes patients at an early age.Our training and
test dataset is an accumulation of 9483 diabetes patients information.The
training dataset is large enough to negate overfitting and provide for highly
accurate test performance.We use performance measures such as accuracy and
precision to find out the best algorithm deep ANN which outperforms with 95.14%
accuracy among all other tested machine learning classifiers.We hope our
high-performing model can be used by hospitals to predict diabetes and drive
research into more accurate prediction models.
