# EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms
## 3 Results and Discussion
### Performance Using PPG Waveform-Based Features
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
\begin{tabular}{c} **Combination of** \\ **Wavelengths** \\ \end{tabular} & **Metrics** & **Random Forest** & **XGBoost** & **LightGBM** & **CatBoost** \\ \hline \multirow{4}{*}{RGB} & Pearson’s r & 0.85 & 0.88 & 0.84 & **0.91** \\  & RMSE (mg/dL) & 22.37 & 18.75 & 20.89 & **16.38** \\  & R2 score & 0.67 & 0.76 & 0.72 & **0.81** \\  & MAE (mg/dL) & **10.45** & 12.60 & 15.25 & 12.03 \\  & MAPE (mg/dL) & **7.02** & 8.77 & 11.54 & 8.99 \\ \hline \hline \end{tabular} Best values are in boldface font.  
\end{table} 
Table 2: Results for the RGB combination of wavelengths using the 54 features. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
\begin{tabular}{c} **Combination of** \\ **Wavelengths** \\ \end{tabular} & **Metrics** & **Random Forest** & **XGBoost** & **LightGBM** & **CatBoost** \\ \hline \multirow{4}{*}{RGB} & [PERSON]’s r & 0.89 & 0.92 & 0.84 & **0.93** \\  & RMSE (mg/dL) & 15.72 & 13.83 & 19.4 & **14.02** \\  & R2 score & 0.81 & 0.84 & 0.72 & **0.86** \\  & MAE (mg/dL) & **10.23** & 10.96 & 15.20 & 10.46 \\  & MAPE (mg/dL) & **7.78** & 8.46 & 11.54 & 8.64 \\ \hline \hline \end{tabular} Best values are in boldface font.  
\end{table} 
Table 3: Results for the RGB combination of wavelengths using selected 20 features out of 54 features. CatBoost enhances its performance using oblivious tress as a base learner, which creates symmetrical decision trees by grouping features into single splits. The feature grouping method in decision trees helps neatly organize data and avoid overfitting, leading to more accurate predictions. CatBoost's design also minimizes the need for extensive hyperparameter tuning and provides superior performance despite longer training periods than models such as XGBoost and random forest. This is a testament to its efficiency in handling feature interactions. Conversely, XGBoost and random forest have progressed with expanded features, but fall short of CatBoost's proficiency. LightGBM also demonstrated a performance improvement after using these combined features from the PPG waveform and IMFs. Note that 15% of the total data were reserved for testing at the initial stage, and the results in Tables 2-4 were based on the test set, so the performance indicates that the models are generalized well. A scatter diagram of the predicted and actual (reference) glucose level values using 50 features obtained by combining PPG waveform-based features with IMFs-based features is shown in Figure 8. The scatter plots in Figure 8 compare the performance of four machine learning models (RF, XGBoost, LightGBM, CatBoost) in predicting blood glucose levels. The RF model shows variability, especially at lower glucose values. XGBoost demonstrates higher accuracy, notably in lower glucose ranges. LightGBM exhibits some accuracy but with notable deviation in high glucose level. CatBoost, however, displays most predictions closely aligned with the ideal line indicating superior accuracy in its predictions. The commonly utilized [PERSON]'s error grid analysis (EGA) has been carried out to confirm the clinical safety of the proposed noninvasive blood glucose estimation method. Figure 9 displays the EGA plot with selected features. In the EGA, Zone A represents the values within 20% of the reference sensor. Zone B contains points that are outside of 20% but would not lead to inappropriate treatment. Zone C are those points leading to unnecessary treatment. Zone D are those points indicating a potentially dangerous failure to detect hypoglycemia or hyperglycemia. Zone E are those points that would confuse treatment of hypoglycemia for hyperglycemia. And the green dots in the EGA represent data points. Most of the data points in case of RF, XGB, and LightGBM are in zone A, while a very small percentage of data are in zone B. In this analysis, especially CatBoost, all data points exist in zone A, supporting the better accuracy and reliability of the method proposed in this study.