Analysis of the relationship between weight and sensor average FPG using 3 methods, time-domain analysis, spatial-domain analysis, and linear regression analysis, over a 3.5-year period from a type 2 diabetes patient based on GHMethod: math-physical medicine (No. 542)

Abstract

Gerald C Hsu

In the author’s previous research reports, he mainly applied physics theories, engineering models, mathematical equations, computer big data analytics and artificial intelligence (AI) techniques, as well as some statistical approaches. The majority of medical research scientists’ published papers he has read thus far are primarily based on statistics tools. As a result, in this article, he selected some basic statistical tools, such as correlation, variance, p-values, and regression analysis to study the predicted fasting plasma glucose (FPG) using his measured weight in early morning as input. The first approach of data analysis, starting in 2015, the author utilized a time-domain analysis tool for his glucose research work. This time-domain model has x-axis for displaying the time units, such as days or years, and y-axis for exhibiting certain biomarker’s amplitude, such as glucose or body weight. He then transforms the time-domain data into the frequency-domain via fast Fourier Transform operation in order to estimate the energy associated with hypoglycemic (high blood sugar) which damages our internal organs to various degrees. Sometimes, he also calculates the correlation coefficient (R) between two biomarker datasets.

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