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Diabits Accuracy 

The Diabits algorithm performed with 93.6% accuracy for OhioT1DM data set.

We use Deep Learning methods to analyze  and  model glucose metabolism. Our approach takes advantage of Neural Networks ability to "learn" and "think" like a pancreas.

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The OhioT1DM was developed for blood glucose level prediction research. The dataset consists of 8 weeks of continuous glucose monitoring via Medtronic sensors and self-reported life-event data for 12 people with type 1 diabetes.

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This dataset is chosen because in addition to blood glucose values, carbohydrate and insulin events are also recorded. 

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We recorded the sensitivity (true positive rate), specificity (true negative rate), and accuracy of our predictions for previously unseen patients. 

The model predictions and the actual blood glucose values were given a label, post prediction. The labels are based on ADA’s recognized blood glucose ranges.

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93.6% Accuracy

96.1%  Specificity

83.6% Sensitivity 

The labels for actual and predicted values were used to calculate the accuracy, specificity, and sensitivity. 

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Predicted vs. Actual 

Below is the partial overlay of actual vs predicted blood glucose values. 

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