Author(s): Gerald C Hsu
In this paper, the author presents his collected data on drinking water quality as a supplementary part of his mathematical metabolism model.
This paper describes a two-parameters linear equation for postprandial plasma glucose (PPG) prediction. The author developed this simplified yet highly accurate equation of predicted PPG to help type 2 diabetes (T2D) patients.
PPG contributes approximately 75% to 80% of HbA1C. Through
the author?s diabetes research, he has identified at least 19
influential factors associated with PPG formation. He further
utilized 8 influential factors and artificial intelligence (AI) optical
physics technology to develop an AI-based Glucometer APP to
predict PPG. Whether the involved factors are 8 or 19, it is a
complicated task for patients to learn, understand, or use in terms
of their diabetes control. Among those factors, as indicated in his
previous publications, carbs/sugar intake amount contributes ~38%
and exercise contributes ~41% of PPG. In summary, these two
primary influential factors contribute ~80% of the PPG formation.
Therefore, the author thought about developing a rather simplified
two-parameters based linear equation to simulate the complex
PPG. Initially, he derived a linear equation as follow:
Linear Equation of PPG = ((Baseline Glucose A) + (Carbs/Sugar
grams * Variable B) - (Walking Steps / Variable C)) * Variable D
Based on the author?s 9-year research findings, along with the
acquired knowledge of food nutrition, the relationship between
exercise and glucose, and using the trial and error approximation
method, he developed the equation. After analyzing the big data
of 1,493 days and 4,479 meals, he has finally identified a set of
optimized combination of baseline glucose A, variables B, C, and
D. He then further verified this equation?s validity by calculating
their linear accuracies (must be greater than 95%) and correlation
coefficients (must be greater than 50%) between measured data
versus both AI-based and Equation-based predictions.
Table 1 shows the final results from this equation?s calculation
of predicted PPG. Its most important data are linear accuracy
(based on daily glucose data) and correlation coefficients (based
on 90-days moving averaged data) between measured PPG vs. AI
predicted PPG and Equation predicted PPG, respectively.
(1) AI-based prediction has 99.95% accuracy (higher than
Equation) and a 68% correlation (lower than Equation) due to
its complicated biochemical inter-relationships among those 8
influential factors.
(2) Equation-based prediction has a lower yet 99.46% accuracy
(slighter lower than AI) and a 75% correlation (higher than AI).
This is due to its simplified model with only 2 primary influential
factors (Table 1).
| Value (mg/dL) | Accuracy (Daily) | Correlation (90 Days) | |
|---|---|---|---|
| Measured PPG | 117.5165 | Baseline | Baseline |
| Predicted PPG | 117.4587 | 99.95% | 68% |
| Liner Equation PPG | 116.8851 | 99.46% | 75% |
| Equation vs predicated | 61% |
(3) Variance of shapes from both time-series analysis and spatial analysis between Equation-based predicted PPG and Fingerpiercing measured PPG have also shown their close relationship of moving trends (Figure 1).
Figure 1: Equation-based PPG Prediction (time -series and spatial analysis)
(4) The correlation coefficient between AI and Equation is 61%, but it is high enough to prove the validity of this equation-based prediction model (Figure 2).
Figure 2: Comparison between AI-based and Equation-based PPG prediction models
This big data analytics derived two-parameters linear equation of PPG prediction model which is very simple for patients to use, while offering a high accuracy for PPG prediction. The author has been continuing his efforts to simplify his glucose prediction models in order to provide a simple and practical tool for T2D patients to use. By offering this streamlined process, the patients will be able to control their diabetes by removing certain resistance or reluctance [1-4].
1. Hsu, Gerald C. (2018). Using Math-Physical Medicine
to Control T2D via Metabolism Monitoring and Glucose
Predictions. Journal of Endocrinology and Diabetes 1: 1-6.
2. Hsu, Gerald C (2018) Using Signal Processing Techniques
to Predict PPG for T2D. International Journal of Diabetes &
Metabolic Disorders 3: 1-3.
3. Hsu, Gerald C. (2018) Using Math-Physical Medicine and
Artificial Intelligence Technology to Manage Lifestyle and
Control Metabolic Conditions of T2D. International Journal
of Diabetes & Its Complications 2: 1-7.
4. Hsu, Gerald C (2018) Using Math-Physical Medicine to
Study the Risk Probability of having a Heart Attack or Stroke
Based on Three Approaches, Medical Conditions, Lifestyle
Management Details, and Metabolic Index. EC Cardiology
5: 1-9.
