Applying Linear and Nonlinear Regression Analysis Models to Study the Possible Relationships between Cancers Risk and CVD/Stroke Risk over a 12-Year Period for a Type 2 Diabetes Patient Based on GH-Method: MathPhysical Medicine (No. 551)

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 regression analyses, to study the predicted Cancer risk probability as the output (dependent variable) by using his MI-based CVD/Stroke risk as the input (independent variable). Since 1/1/2012, the author has been collecting various data related to his health (~3 million data) which includes 4 categories of medical conditions, obesity, diabetes, 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). Due to his limited knowledge in earlier years, the datasets from 2010 to 2012 are incomplete; therefore, the data used in this study for the initial period of 2010-2012 are his best-guessed data.

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