Applying Multiple Regression Analyses Model to Predict Fasting Plasma Glucose in Early Morning based on 3 Independent Variables, Sleep Score, Weight in the Early Morning, and Daily HbA1C over a 6.5-Year Period for a type 2 Diabetes Patient Based on GH-Method: Math-Physical Medicine (No. 548)

Abstract

Gerald C Hsu

In the author’s previous medical 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 to explore and interpret various biophysical phenomena. However, the majority of medical research papers he has read thus far are primarily based on statistics. As a result, in this article, he selects some basic statistical tools, such as correlation, variance, p-values, and multiple regression analyses, to study the predicted fasting plasma glucose (FPG) in early morning as the output (dependent variable) by using three independent variables, sleep score, weight in the early morning, and daily HbA1C as inputs. Since 5/1/2015, the author has been collecting various data related to his medical conditions including body weight, blood pressure, blood lipids, and glucose along with sleep conditions and many other lifestyle details.

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