# EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms
## 3 Results and Discussion  
### Performance Using PPG Waveform-Based Features  
The dataset provided in Table 1 was used to evaluate the performance of four machine learning models: random forest (RF), XGBoost, LightGBM, and CatBoost in estimating blood glucose levels. These specific algorithms were selected based on their proven effectiveness in comparable prediction tasks [27; 28]. Their robust nature and ability to identify complex patterns in data, mentioned in [29], also contributed to their selection. To reduce the risk of overfitting, careful adjustment of hyperparameters was undertaken. Additionally, the separation of training and testing data allowed for close monitoring of both training and testing errors. It was observed that in no instances did the training error fall below the testing error which indicates the overfitting. These four algorithms were evaluated utilizing a set of 54 features on the test dataset and leave one out cross validation (LOOCV) method was used for training dataset. Table 2 displays the relationship between predicted and actual blood glucose values using various regression metrics. This includes the Pearson correlation coefficient (Pearson's r), along with other important measures suchas root mean squared error (RMSE), R2 score, mean absolute percentage error (MAPE) and mean absolute error (MAE). From Table 2, we can see that the CatBoost algorithm performs better than other algorithms when we use PPG waveform-based features. The fact that the random forest (RF) model exhibits a lower mean absolute error (MAE) and MAPE compared to other algorithms may be attributed to the variance in performance variation over a wide range of glucose values. Afterwards, feature importance-based selection was done for four algorithms. The 20 features depicted in Figure 5 were applied as input to the regression algorithms and results are shown in Table 3.  
After feature selection, the performance of RF, XGB, and CatBoost improved, however the performance of LightGBM was almost the same. LightGBM is known for handling large sets of features well due to its efficient implementation of gradient boosting, and its performance does not improve much when the number of features is reduced. Performance Using 50 Features (Combining Top 20 PPG Waveform-Based Features with Top 30 IMF-Based Features)  
In the context of this study, features derived from intrinsic mode functions (IMFs) are utilized to encapsulate the intrinsic oscillatory modes inherent in PPG signals. In contrast, features based on the PPG waveform primarily capture the overall characteristics of the signal. Integrating these two types of features is intended to provide a comprehensive representation of the information content of the signal. Towards this goal, the feature set was expanded to include 50 features, consisting of the top 20 PPG waveform-based features illustrated in Figure 5 along with the top 30 IMF-based features in Figure 6.  
Expanding the feature set to 50 by combining PPG waveform-based features and IMF-based features significantly improved the performance of random forest, with Pearson's r of 0.94, as shown in Table 4. The performance improvement observed with XGBoost having Pearson's r of 0.95 indicates that the inclusion of additional features provided valuable insights. Similarly, LightGBM also showed notable improvement, with Pearson's drift r performance increasing from 0.89 to 0.93. Pearson's r of CatBoost improved to 0.96, maintaining its best performance position with the expanded feature set.