# Overcoming Individual Discrepancies, a Learning Model for Non-Invasive Blood Glucose Measurement
## 6 Experiment and Results
### Data Acquisition  
The purpose of data acquisition is to obtain a training dataset and a testing dataset, for ease of training the algorithm through these datasets and testing the accuracy of blood glucose estimation. For data gathering, each record includes nine attributes, of which the first one is the record's ID, and the second to the sixth gathering from our prototype are absorbance data of earlobe at each wavelength of 490 nm, 660 nm, 730 nm, 850 nm, and 930 nm. The seventh is blood glucose concentration for reference which is collected by a standard invasive glucose meter. The eighth attribute is the name of subject, and the ninth and tenth are date and time of the collection.  
The data acquisition experiment is performed during September of the year 2017 from 1 st to 15 st. There are six yellow race adult subjects participating in the experiment, among them we have five diabetic patients and one healthy individual. The characteristics of each subjects are presented in Table 3. The prototype designed in this paper and a \(SANNU^{TM}\) invasive glucose meter are adopted  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Glucose Estimator**} & \multirow{2}{*}{**with ICA?**} & \multicolumn{2}{c}{**Best Parameters**} & \multirow{2}{*}{**Best \(Corr\)**} & \multicolumn{2}{c}{**Number of Training Samples**} \\ \cline{4-5}  & & & & \(Corr=0.5\) & **Best \(Corr\)** \\ \hline \multirow{2}{*}{Random Forest [18]} & Yes & n\_estimator = 200 & 0.819 & 10 & 60 \\  & No & max\_depth = 2 & 0.523 & 20 & 41 \\ \hline \multirow{2}{*}{Adaboost [30]} & Yes & base\_estimator = DT & 0.695 & 8 & 52 \\  & No & n\_estimator = 20 & 0.565 & 7 & 49 \\ \hline \multirow{2}{*}{kNN [31]} & Yes & n\_neighbors = 3 & 0.461 & \(Inf\) & 49 \\  & No & 0.642 & 23 & 43 \\ \hline \multirow{2}{*}{Decision Tree [32]} & Yes & max\_depth = 20 & 0.538 & 11 & 44 \\  & No & 0.407 & \(Inf\) & 46 \\ \hline \multirow{2}{*}{SVM [33]} & Yes & C = 0.5, Kernel = rdf & 0.119 & \multirow{2}{*}{\(Inf\)} \\  & No & tol = 0.001 & 0.154 & \\ \hline \hline \end{tabular} \(Inf\) indicates that the method does not converge.  
\end{table}
Table 2: Comparative experiment results of various regression methods.  
as data acquire equipment. We obtain blood glucose data from each subject five times a day, which happened at morning on an empty stomach, 2 h after breakfast, right before lunch, 2 h after lunch, and right before dinner. During the data acquisition process, due to personal reasons of some subjects, such as physical examination, outing, etc., blood glucose measurement cannot be performed at the specified time, resulting in some data missing. In addition, we have excluded some abnormal data due to hardware reasons, such as signal loss, signal anomalies, etc. Finally, 286 valid records are received after eliminating unmeasurable or invalid data. Among these records, 84 records that are rather complete come from a subject on focus tracking who is labeled as subject #1, while the remaining 202 records come from the other five subjects who are labeled as subject #2 to subject #6.