A1C Variance Study and PPG Prediction Methodology over Six Periods Using GH-Method: Math-Physical Medicine

Abstract

In this case study, the author analyzed, predicted, and interpreted a type 2 diabetes (T2D) patient’s hemoglobin A1C variances based on six periods data utilizing the GH-Method: math-physical medicine approach by applying mathematics, physics, engineering modeling, and computer science (big data analytics and AI). He believes in “prediction” and has developed five models, including metabolism index, weight, fasting plasma glucose (FPG), postprandial plasma glucose (PPG), and hemoglobin A1C. All prediction models have reached to 95% to 99% accuracy. His focus is on preventive medicine, especially on diabetes control via lifestyle management.