cancer-primary-healthcare-2019-keynote

Volume 2

Journal of Molecular Cancer

Cancer & Primary Healthcare 2019

May 20-21, 2019

Page 13

Cancer Research & Oncology

Primary Healthcare and Medicare Summit

May 20-21, 2019 | Rome, Italy

Global Meet on

World Congress on

Gerald C Hsu

EclaireMD Foundation, USA

J Mol Cancer, Volume 2

From a public health point of view to investigate the control of obesity, diabetes, and

cardiovascular risk via nutrition and exercise (GH-Method: math-physical medicine)

Introduction

:In 2017, public health data revealed that the United States had 2 million deaths which included diabetes, heart

diseases, stroke, and nephrosis that occupied 45% (~907,000) of this number. Furthermore, >85% of type 2 diabetes (T2D)

patients are overweight and >50% are obese.

Methods:

The author spent 23,000 hours during the past 8.5 years using math-physical medicine to conduct his research. He

has collected and processed ~1.5 million data, including ~300,000 medical conditions, and ~1.2 million lifestyle details. He

then utilized the GH-Method: math-physical medicine (MPM) which involves advanced mathematics, optical physics, signal

processing, energy and wave theories, statistics, big data analytics, machine learning, artificial intelligence to develop five

prediction models, including weight, FPG, PPG, adjusted glucose, and HbA1C.

Results:

His clinical case studies have offered the following results:

(1) BMI reduction from 32 (obese) to 24.7 (normal)

(2) FPG reduction from ~200 mg/dL to ~105 mg/dL; PPG from 279 mg/dL to 119 mg/dL; Daily average glucose from >250 mg/

dL to ~116 mg/dL; HbA1C from 10% to <6.5%

(3) Risk reduction of having cardiovascular diseases and stroke from 74% prior to 2010 (suffered 5 cardiac episodes) to 26.4%

in 2017.

(4) Averaged carbs/sugar intake amounts (38% contribution on PPG): 14.5 gram/meal and ~60 grams/day (low carb diet).

Exercise amount (41% contribution on PPG): 4,300 steps/meal and 18,000 steps/day.

Conclusion:

His MPM methodology and prediction models (>99% accuracy) are proven to be effective tools on controlling

T2D. His flow diagram can also provide an effective guidance to patients to control and improve their conditions on obesity,

diabetes, and heart problems. These technology-based prediction and prevention models can be used as educational tools to help

diabetes patients through public-health platforms, channels and programs.

Biography

The author received an honourable PhD in mathematics and majored in engineering at MIT. He attended different universities over 17 years

and studied seven academic disciplines. He has spent 20,000 hours in T2D research. First, he studied six metabolic diseases and food

nutrition during 2010 to 2013, then conducted his own diabetes research during 2014 to 2018. His approach is “quantitative medicine” based

on mathematics, physics, optical and electronics physics, engineering modelling, signal processing, computer science, big data analytics,

statistics, machine learning, and artificial intelligence. His main focus is on preventive medicine using prediction tools. He believes that the

better the prediction, the more control you have.