  According to the American Diabetes Association(ADA), 30.3 million people in
the United States have diabetes, but only 7.2 million may be undiagnosed and
unaware of their condition. Type 2 diabetes is usually diagnosed for most
patients later on in life whereas the less common Type 1 diabetes is diagnosed
early on in life. People can live healthy and happy lives while living with
diabetes, but early detection produces a better overall outcome on most
patient's health. Thus, to test the accurate prediction of Type 2 diabetes, we
use the patients' information from an electronic health records company called
Practice Fusion, which has about 10,000 patient records from 2009 to 2012. This
data contains individual key biometrics, including age, diastolic and systolic
blood pressure, gender, height, and weight. We use this data on popular machine
learning algorithms and for each algorithm, we evaluate the performance of
every model based on their classification accuracy, precision, sensitivity,
specificity/recall, negative predictive value, and F1 score. In our study, we
find that all algorithms other than Naive Bayes suffered from very low
precision. Hence, we take a step further and incorporate all the algorithms
into a weighted average or soft voting ensemble model where each algorithm will
count towards a majority vote towards the decision outcome of whether a patient
has diabetes or not. The accuracy of the Ensemble model on Practice Fusion is
85\%, by far our ensemble approach is new in this space. We firmly believe that
the weighted average ensemble model not only performed well in overall metrics
but also helped to recover wrong predictions and aid in accurate prediction of
Type 2 diabetes. Our accurate novel model can be used as an alert for the
patients to seek medical evaluation in time.
