A Summary Report Utilizing Math-Physical Medicine Models and Statistical Regression Models to Derive Practical Prediction Equations of Selected Biomarkers for Glucoses, A1C, Diabetes, and Risk Probabilities of Having Certain Chronic Disease Complications from the Collected Data of a Type 2 Diabetes Patient based on GH-Method: Math-Physical Medicine (No. 560)

Abstract

Gerald C Hsu

In the author’s previous medical research reports, he mainly applied physics theories, engineering models, mathematical equations, and computer science tools, including trend and pattern analysis, big data analytics and artificial intelligence (AI) techniques, as well as some statistical approaches to explore and interpret various biophysical phenomena. His physics and engineering methodologies include wave theory, energy theory, quantum mechanics, optical physics, linear elasticity theory, and finite element method. His mathematics methodologies include topology, nonlinear algebra, geometric algebra, perturbation theory, Fourier transform, trend and pattern analyses, statistics, and probability theory. However, the majority of medical research papers he has read thus far are primarily based on statistics tools, such as regression analysis, probability calculation, etc. As a result, he decided to dedicate the month of November 2021 using the same statistical regression models similar to other traditional medical papers to analyze his collected biomarkers to verify the relationship validity of his previous research results based on his developed math-physical models. During the month, he conducted 18 regression studies in papers No. 540 -544, No. 546-553, and No. 555-559. In the regression studies, he selected some basic statistical tools, such as correlation, variance, significance F value, p-value, and regression analyses (linear and nonlinear, single or multiple variables), to study the behaviors and relationships of his collected biomarkers. The regression model mentioned above is a statistical model that uses values such as mean, standard deviation, correlation, variance, significance F, p-value, and the equation of “Y = y-intercept + slope*X”. These regression models include linear and nonlinear regression. The nonlinear regression models include exponential, logarithmic, polynomial, and power. Since 1/1/2012, the author has collected ~3 million data regarding his health conditions, lifestyle details, internal organs, and chronic diseases.

PDF