Prediction of Diabetes Progression Using Statistical Modeling In the United States of America, more than 100 million adults currently live with diabetes mellitus as well as prediabetes, a condition that occurs as a result of elevated blood sugar levels. Diabetes mellitus can be identified in a series of laboratory tests where blood must be drawn to analyze a multitude of parameters such as Glucose, Hemoglobin A1C (HGBA1C), Alanine transaminase (ALT), Cholesterol (CHOL), Highdensity lipoprotein (HDL), Low density lipoprotein calculated (LDL_CALC), and Triglyceride (TRIG). This disease must be identified in its early stages because diabetes mellitus can lead to heart and blood vessel problems which may be fatal to the patient. This research looks at a dataset spanning ten years of a patient comprehensively to see the patterns established and how they can be useful in advising lifestyle and routine changes to prevent diabetes mellitus of a patient. In order to predict the Diabetes Mellitus parameters, statistical computation and graphical visualization was implemented using the xe2x80x9cRxe2x80x9d programming language. In R, different approaches were taken to explore the data in two different ways, the parameters versus the data. The parameters were looked at with the principal component analysis (PCA) approach to understand the effects of critical parameters to the patient. The data was analyzed with regards to the integrity of the patientu0027s health as well as potential symptoms they may experience. The clustering technique was used to group the data in a way that visually represented the interactions amongst the points. Clustering serves as an easy way to organize data and detach it from details that may not be as important as the points themselves. PCA and clustering are purely descriptive statistics that can tell a physician about the patientu0027s current health condition, but does not give them information about future symptoms. In order to create predictive models to foresee how the parameters will fluctuate over the next ten years, Vector Auto Regressive models (VARS) can be utilized. These additive models can inform a physician about how the patientu0027s parameters are progressing within a time frame. The prediction results are within three standard deviation values which will provide the low and high range values when generating the models.

Model Fusion to Enhance the Clinical Acceptability of LongTerm Glucose Predictions This paper presents the Derivatives Combination Predictor (DCP), a novel model fusion algorithm for making longterm glucose predictions for diabetic people. First, using the history of glucose predictions made by several models, the future glucose variation at a given horizon is predicted. Then, by accumulating the past predicted variations starting from a known glucose value, the fused glucose prediction is computed. A new loss function is introduced to make the DCP model learn to react faster to changes in glucose variations. The algorithm has been tested on 10 insilico type1 diabetic children from the T1DMS software. Three initial predictors have been used: a Gaussian process regressor, a feedforward neural network and an extreme learning machine model. The DCP and two other fusion algorithms have been evaluated at a prediction horizon of 120 minutes with the rootmeansquared error of the prediction, the rootmeansquared error of the predicted variation, and the continuous glucoseerror grid analysis. By making a successful tradeoff between prediction accuracy and predictedvariation accuracy, the DCP, alongside with its specifically designed loss function, improves the clinical acceptability of the predictions, and therefore the safety of the model for diabetic people.