Applying Linear Regression Analysis Model to Predict HbA1C Values Using the Daily Estimated Average Glucose (eAG) Values from a 14-Month Period for a type 2 Diabetes Patient based on GH-Method: Math-Physical Medicine (No. 559)

Abstract

Gerald C Hsu

Since 1/1/2012, the author has been collecting various biomedical and lifestyle data related to his health conditions (~3 million data) which includes 4 categories of medical conditions, 4 chronic diseases consisting of obesity, diabetes (via finger-piercing glucose and HbA1C), hypertension, and hyperlipidemia (m1 through m4), along with 6 categories of lifestyle details, including exercise, water intake, sleep, stress, food, and daily life routines (m6 through m10). Starting on 1/1/2013, he accumulates 4 glucose data per day, one for fasting plasma glucose (FPG) and 3 for postprandial plasma glucose (PPG). In addition, beginning on 5/8/2018, his glucoses are automatically measured using a continuous glucose monitoring (CGM) sensor device to collect 96 glucose data per day. Over the past 12 years, he has tested for his HbA1C value each quarter since 2010. Through research work on type 2 diabetes (T2D) from 2015 to 2017, he developed several math-physical models to predict his HbA1C values prior to labtests for HbA1C at a clinic or hospital. He compares his predicted HbA1C values against the lab-tested HbA1C values and has achieved a 99% to 100% prediction accuracy.

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