Risk Probability of Having a Cardiovascular Disease, Stroke, or Renal Complications Using Annual Segmented Data of Glucose and Metabolism Index (GH Method: Math-Physical Medicine)

Abstract

In 2014, the author applied topology concept, finite-element engineering technique, and nonlinear algebra operations to develop a mathematical metabolism model, which contains ten categories including four output categories (weight, glucose, BP, other labtested data including lipids & ACR) and six input categories (food, water drinking, exercise, sleep, stress, routine life patterns and safety measures). These 10 metabolic categories include approximately 500 detailed elements. He further defined a new 
parameter referred to as the metabolism index (MI) that has a combined score of the above metabolic categories and elements. Since 2012, he has collected and stored ~2 million data from his own body health conditions and personal lifestyle details. He then developed a set of algorithms which include a patient’s baseline data, such as age, race, gender, family genetic history, medical history, and bad habits along with conducting the 
following three calculations: 1. Medical conditions - individual M1 through M4: i.e. obesity, diabetes, hypertension, hyperlipidemia and others.
 2. Lifestyle details - individual M5 through M10. 3. MI scores - a combined score of M1 through M10.