Applying Linear Regression Analysis Model to Compare the Outputs of 3 Regression Predicted CVD/Stroke Risk Probabilities Using 3 Different Inputs Which are the Calculated Sensor HbA1C value over 14-Months, Combined Medical Condition Score Months, and Calculated Finger HbA1C over an 8-Year Period from the Collected Data of a type 2 Diabetes Patient Based on GH-Method: Math-Physical Medicine (No. 557)

Abstract

Gerald C Hsu

Since 1/1/2012, the author has been collecting various biomedical and lifestyle of ~3 million data related to his health conditions. This includes the medical categories for 4 chronic diseases of obesity, diabetes, hypertension, and hyperlipidemia (m1 through m4), along with 6 categories of lifestyle details, including exercise, water intake, sleep, stress, food, and daily life routines (m6 through m10). In early 2018, he studied, researched, and published many articles regarding the risks of having CVD/Stroke based on his developed metabolism index (MI) model. In this paper, he will compare the calculated CVD risks based on the MI model through his developed GH-method: math-physical medicine methodology against the recently calculated 3 CVD risk probabilities based on a traditional statistical regression model but using 3 different input datasets. In this article, he will not repeat the detailed introduction of the regression analysis in the Method section because it is available in many statistics textbook. It should be noted that in regression analysis, the correlation coefficient R should be > 0.5 or 50% to indicate a strong inter-connectivity and the p-value should be <0.05 to be considered as statistically significant.

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