Math-Physical Medicine

NO. 016

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

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

The author has extended his 8.5-year T2D research to focus on the relationship between lifestyle management for managing metabolic diseases and the risk probability of having a heart attack or stroke.

He has developed several big data based numerical simulation models using ~1.5M data he collected. Initially, he chose age, gender, race, family history, smoking, drinking, drug abuse, personal medical history,  and weight/waistline to establish a static baseline.  He then developed a mathematical simulation model to combine all key elements of lifestyle management, including food, exercise, stress, sleep, water intake, life routine pattern to conduct his dynamic computations.  He utilized 332,150 data of these six categories within the past 2,555 days (1/1/2012 – 12/31/2018) to compute the probability of having a heart attack or stroke in the near future.  Finally, he conducted sensitivity analyses to cover the probability variance using different weighting factors (WF).

Comparing the results from the worst year, 2000, to the health-improving period of 2012-2018, the probability values are:

  • 2000 with BMI 31:  83% (Three episodes of chest pain during 2001-2006)
  • 2012 with BMI 29:  70%
  • 2018 with BMI 25:  33% (Normalization Range: 0% – 100%)

In summary, over eight years, he has an average of 34% probability with +/- 18% variance of WF sensitivity.

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 future.