Using the Fasting Blood Sugar and Glycated Haemoglobin Models for Predicting the Personal Management of Type-2 Diabetes

Abstract

Purpose
 Personal management of type-2 diabetes mellitus (T2DM) is very important in preventing complications of other diseases for infected patients. We aimed to develop the mathematical models for predicting the performance of personal T2DM management.
 Methods
 We proposed three hypotheses as the basic principles to develop the fasting blood sugar (FBS) and glycated haemoglobin (HbA1c) models. We tested and validated these two models using the FBS and HbA1c data collected from two clinical laboratories in Malaysia and one clinical laboratory in Indonesia from 16 March 2009 to 29 May 2016.
 Results
 Using the power FBS model permits us to predict the FBS level of below 6 mmol/L after 1277 days of the T2DM management. Using the logarithmic HbA1c model permits to predict the HbA1c level in blood of below 6.4% after 2553 days of the T2DM management but never reaches below 6%. We verified that correlation between the FBS and HbA1c data is able to get an excellent fit with linear regression.
 Conclusions
 Person with T2DM can set goals for managing his disease and lifetime metabolic control to get better health-related quality of life (HRQoL) and should pay attention to the main determinants of HRQoL. Using the FBS and HbA1c models as strategic analysis tool permits us to predict the effectiveness of T2DM management with an ultimate improvement in his quality of life to take care of himself.

Assessment of economic inequality during three decades in Aceh Province of Indonesia

“…The formulation of linear model was able to predict the response variable of glycated haemoglobin using the explanatory variable of fasting blood sugar (Fulazzaky and Fulazzaky, 2019) and to estimate the rate constant of biochemical reaction in the various leachates (Fulazzaky, 2013). An extension of linear modelling process that allows a model to describe a set of secondary data collected from the BPSAP o ce would be able to follow the probability distribution other than normal distribution.…”