In the field of medical research, the hidden biophysical behaviors and possible inter-relationships exist among lifestyle details, medical conditions, chronic diseases, and certain medical complications, such as heart attacks, stroke, cancers, dementia, and even longevity concerns.  He has noticed that most medical subjects with their associated data, multiple symptoms, and influential factors are “time-dependent” which means that all biomedical variables change from time to time because body living cells are organic and dynamically changing.  This is what Professor Norman Jones, the author’s adviser at MIT, suggested to him in December of 2021 and why he utilizes the VGT tools from physics and engineering to conduct his medical research work since then. 

In this article, since the metabolism index values of m1 (body weight or obesity), m9a (food quality), and m5 (walking exercise) have already been normalized in the process of data entry, therefore these three normalization factors for weight, exercise, and food quality are 1.0. 

It should also be mentioned that the author has developed a mathematical model for estimating the risk probability of developing various cancers based on his developed metabolism index (MI) model in 2014.  His MI model contains 10 categories, 4 medical conditions, i.e. obesity, diabetes, hypertension, and hyperlipidemia, and 6 lifestyle details, i.e. food & diet, drinking water, exercise, sleep, stress, and daily life routines.  This MI model calculates a daily MI score based on all input health data.  Furthermore, based on certain special influences, stimulators, and situations, this MI model can then be further modified and turned it into an estimation tool for a variety of cancers or medical complications, such as heart attack, stroke, kidney failure, retinopathy, etc.  

Gerald C. Hsu received an honorable Ph.D. in mathematics and majored in engineering at MIT. He attended different universities over 17 years and studied seven academic disciplines. He has spent 20,000 hours in T2D research. First, he studied six metabolic diseases and food nutrition during 2010-2013, then conducted research during 2014-2018. His approach is “math-physics and quantitative medicine” based on mathematics, physics, engineering modeling, signal processing, computer science, big data analytics, statistics, machine learning, and AI. His main focus is on preventive medicine using prediction tools. He believes that the better the prediction, the more control you have.