  To best of the authors knowledge, this article presents the first-ever
non-invasive glucometer that takes into account serum glucose for high
accuracy. In case of blood glucose measurement, serum glucose value has always
been considered precise blood glucose value during prandial modes. Serum
glucose can be measured in laboratory and more stable glucose level compare to
capillary glucose. However, this invasive approach is not convenient for
frequent measurement. Sometimes, Conventional invasive blood glucose
measurement may be responsible for cause of trauma and chance of blood related
infections. To overcome this issue, in the current paper, we propose a novel
Internet-of-Medical (IoMT) enabled glucometer for non-invasive precise serum
glucose measurement. In this work, a near-infrared (NIR) spectroscopic
technique has been used for glucose measurement. The novel device called iGLU
2.0 is based on optical detection and precise machine learning (ML) regression
models. The optimal multiple polynomial regression and deep neural network
models have been presented to analyze the precise measurement. The glucose
values of serum are saved on cloud through open IoT platform for
endocrinologist at remote location. To validate iGLU 2.0, Mean Absolute
Relative Difference (mARD) and Average Error (AvgE) are obtained 6.07% and
6.09%, respectively from predicted blood glucose values for capillary glucose.
For serum glucose, mARD and AvgE are found 4.86% and 4.88%, respectively. These
results represent that the proposed non-invasive glucose measurement device is
more precise for serum glucose compared to capillary glucose.
