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June 24-25, 2019 | Philadelphia, USA
DIABETES, ENDOCRINOLOGY, NUTRITION
AND NURSING MANAGEMENT
2
nd
International Conference on
Diabetes Congress 2019
Journal of Diabetology | Volume 3
Page 8
OF EXCELLENCE
IN INTERNATIONAL
MEETINGS
alliedacademies.comYEARS
21,000 data and then re-integrated them into three distinctive PPG waveform types which revealed different personal-
ity traits and psychological behaviors of type 2 diabetes patients. For single time-stamped variables, he used tradition-
al time-series analysis. For interactions between two variables, he used spatial analysis. Furthermore, he also applied
Fourier Transform to conduct frequency domain analyses to discover some hidden characteristics of glucose waves. He
then developed an AI glucometer tool for patients to predict their weight, FPG, PPG and A1C. It uses various computer
science tools, including big data analytics, machine learning (self-learning, correction and simplification) and artificial
intelligence to achieve very high accuracy (95% to 99%).
Results:
In 2010, his average glucose was 280 mg/dL and A1C was >10%. Now, his glucose value is 116 mg/dL and A1C
is 6.5%. Since his health condition is stable, he no longer suffers from repetitive cardiovascular episodes.
Conclusion:
Instead of utilizing traditional biology, chemistry and statistics, the methodology of GH-Method:
math-physical medicine uses advanced mathematics, physics concept, engineering modeling and computer science
tools (big data analytics, artificial intelligence), which can be applied to other branches of medical research in order to
achieve a higher precision and deeper insight.
Fig.1. Comparison of MPM vs. BCM