## 3 Background
### Binary Harris Hawks Optimizer (BHHO)
This reduction of 73.91% in the feature set significantly decreases computational demands while enhancing predictive accuracy. In contrast to the ETR model, which utilized all 69 features and achieved an RMSE of 16.52 mg/dL, an MAE of 14.61 mg/dL, and a CEG Zone A percentage of 76.32%, the IDA-LightGBM model demonstrated substantial improvements across all key metrics. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Matrix**} & **Selected** & **Fitness** & **RMSE** & **MAE** & **Time** \\  & & **Features** & **(Zone A\%)** & **(mg/dL)** & **(mg/dL)** & **Costs (Sec)** \\ \hline \multirow{3}{*}{IDA-ETR} & Mean & **14.9** & 91.05 & **15.375** & 13.345 & 321.32 \\ \cline{2-7}  & STD & **2.07** & **1.29** & 0.81 & 0.85 & **2.96** \\ \cline{2-7}  & Best & **13** & 92.11 & **14.39** & **12.43** & 319.15 \\ \hline \multirow{3}{*}{IDA-LightGBM} & Mean & 20.2 & **92.89** & 15.48 & **13.30** & **311.72** \\ \cline{2-7}  & STD & 3.34 & 1.68 & **0.47** & **0.42** & 5.88 \\ \cline{1-1} \cline{2-7}  & Best & & & 15.76 & 13.96 & \multirow{3}{*}{**298**} \\ \cline{1-1} \cline{2-7}  & Best & & **94.74** & & & \\ \cline{1-1} \cline{2-7}  & (Modified) & & & 15.36 & 13.17 & \\ \hline \end{tabular}
\end{table} 
Table 6: Comparative analysis of best-performing models during phase 2 in terms of selected features, fitness, RMSE, MAE, and time costs, evaluated on the testing split of the dataset. The data showcasing the best performance for each metric are highlighted in bold. Specifically, the IDA-LightGBM model achieved an RMSE of 15.36 mg/dL, an MAE of 13.17 mg/dL, and a significantly higher Zone A percentage of 94.74%, indicating a 24.13% enhancement in clinically relevant predictions over the full feature set. Furthermore, the IDA-LightGBM model ensured that no predictions fell into dangerous CEG Zones, further confirming its clinical reliability. Hyperparameter tuning of the LightGBM model also contributed to these enhancements. The increase in boosting iterations (n_estimators) to 670 improved the model's ability to capture complex patterns within the reduced feature set. Moreover, the L1 regu

Figure 11: CEG analysis of the best performing models of phase 2: (**a**) best IDA-ETR; (**b**) best modified IDA-LightGBM (best overall model). Figure 10: Convergence curves of the best performing models of phase 2: (**a**) best IDA-ETR; (**b**) best modified IDA-LightGBM (best overall model). larization parameter (reg_alpha) was modified to 3.1, reducing overfitting and promoting sparsity. This optimized configuration achieved an effective balance between computational efficiency and predictive accuracy. These findings are illustrated in Table 7, which compares actual and predicted glucose values, highlighting the model's potential for non-invasive diabetes management applications. \[D=\left[ \begin{array}{cc}\text{PPg\_Rate\_SD}&SD1d&\text{SDRMSSD}&\text{ MeanNN}&FuzzEn\\ \text{SDSD}&\text{CVN}&TP&CVI&\text{MinNN}\\ \text{MadNN}&\text{MCVNN}&PI&S1&\text{MaxNN}\\ \text{ShanEn}&\text{Prc20 NN}&MFDFA\_alpha1\_Mean&\end{array} \right] \tag{22}\]

### Comparative Performance Analysis of Proposed Models with Existing Studies Utilizing PPG Signals

This section compares the proposed IDA-LightGBM model for non-invasive BGL estimation against existing studies utilizing PPG signals, focusing on accuracy, clinical relevance, simplicity, and practicality. A summary is provided in Table 8.

The IDA-LightGBM model achieved an RMSE of 15.36 mg/dL, MAE of 13.17 mg/dL, and 94.74% predictions in CEG Zone A, outperforming previous models in both predictive accuracy and clinical safety. Unlike other multisensor approaches that use electrodermal