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• W3005069949 abstract "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), High-density 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 “R” 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 patient's 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 patient's 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 patient's 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." @default.
• W3005069949 created "2020-02-14" @default.
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• W3005069949 date "2019-11-01" @default.
• W3005069949 modified "2023-09-25" @default.
• W3005069949 title "Prediction of Diabetes Progression Using Statistical Modeling" @default.
• W3005069949 doi "https://doi.org/10.1109/bibm47256.2019.8983184" @default.
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