Background & Aim:
The author has extended his 8-year type 2 diabetes (T2D) research to focus on the relationship between lifestyle for managing metabolic diseases and the probability of having a heart attack or stroke.
Material & Method:
He has developed several big data numerical simulation models using ~1.5M data. Initially, he chose age, gender, race, family history, smoking, drinking, drug abuse, medical health conditions, 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 to conduct his dynamic computations. He utilized 295,620 data of these six categories within the past 2,274 days 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, within eight years, he has an average of 34% probability with +/- 18% variance of WF sensitivity.
The mathematical simulation results are validated by past health examination reports. This big data dynamic simulation approach using math-physical medicine will provide an early warning to patients with chronic disease of having a heart attack or stroke in the future.