Math-Physical Medicine

NO. 017

Using GH-Method: math-physical medicine to Study the Risk Probability of Having a Heart Attack or Stroke Based on Metabolism Index

Corresponding Author: Gerald C. Hsu, eclaireMD Foundation, USA.

The author has extended his 8.5-year T2D research along with ~1.5 M data he collected to examine the relationship among metabolism index (MI), general health status unit (GHSU: a 90-days moving average of MI), and the risk probability of having a heart attack or stroke.

In 2014, he researched and built a mathematical models for MI and GHSU to understand and measure the multiple interactions between four metabolic disease output categories and six lifestyle input categories.

He omitted genetic influences, personal habits, and past health conditions in order to focus on the dynamic changes of these 10 input and output categories with a total of ~500 elements.  He utilized ~1.5M data within the past 2,555 days (1/1/2012 – 12/31/2018) to calculate  the risk probability of having a heart attack or stroke.  He also conducted research work based on medical conditions output and lifestyle management input separately.  However, in this study, he performed an integrated input/output research.  He used 80% of integrated results to compare with other two results objectively.

Comparing the results from a period between 2012 to 2018, the probability values are:

From 74% (2012) with a decrease trend year after year, to 33% (2018), with an average of 52% over past 8 years.  (Normalization Range: 0% – 100%)

The mathematical simulation results are validated by his past health examination reports.  This big data based dynamic simulation approach using GH-Method: math-physical medicine will provide an early warning to patients with chronic disease of having a heart attack or stroke in the near future.