## 3 Background
### Binary Harris Hawks Optimizer (BHHO)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Actual BGL** & **Predicted BGL** & **Difference** & **Difference** \\
**(mg/dL)** & **(mg/dL)** & **(mg/dL)** & **(percentage)** \\ \hline
68.4 & 70.28 & 1.88 & 2.75 \\
86.4 & 93.68 & 7.28 & 8.43 \\
97.2 & 110.12 & 12.92 & 13.29 \\
109.8 & 115.85 & 6.05 & 5.51 \\
122.4 & 124.75 & 2.35 & 1.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance measure for the best BGL estimation model (IDA-LightGBM), evaluated on the testing split of the dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Reference** & **Algorithm** & **RMSE** & **MAE** & **Zone A** & **Input** & **Features** & **FS Method** & **Feature** & **Number** & **Detection** \\ \hline [PERSON] et al. [7] & DNN & 26.85 & & 99.49\% & ECG and PPG & Three PS & & 21 & 39.64-392.8 \\ \hline C.-Y. Liao and W.-C. & \multirow{3}{*}{LRCN} & \multirow{3}{*}{11.146} & \multirow{3}{*}{4.7} & \multirow{3}{*}{99\%} & PPG & Convolutional & & & \\ Fang [8] & & & & & (I min) & layers & & & \\ \hline \multirow{3}{*}{[PERSON] et al. [5]} & \multirow{3}{*}{BPNN} & \multirow{3}{*}{5.08} & \multirow{3}{*}{6.38} & \multirow{3}{*}{100\%} & PPG \& & & & \\  & & & & & (3 min) & & & \\  & & & & & & \& physiological & & \\ \hline [PERSON] et al. [6] & PLS & 17.71 & & & & & & \\  & & & & & & (60 s) & & \\ \hline \multirow{3}{*}{[PERSON] et al. [9]} & \multirow{3}{*}{CNN} & \multirow{3}{*}{26.1} & \multirow{3}{*}{17.26} & \multirow{3}{*}{100\%} & 100\% & PPG & & & \\  & & & & (60 s) & & & \\  & & & & & (200 s) & & & \\ \hline \multirow{3}{*}{[PERSON] et al. [10]} & \multirow{3}{*}{BET} & \multirow{3}{*}{30.74} & \multirow{3}{*}{22.90} & 100\% & PPG, BP & & & \\  & & & & & & demographic & & \\ \cline{1-1}  & & & & & & & \\ \cline{1-1}  & & & & & & & \\ \hline [PERSON] et al. [11] & SVR & 18.66 & 16.45 & & PPG, BMI, & & Filtering and & 20 & 5 & \\ \hline \multirow{3}{*}{[PERSON] et al. [12]} & \multirow{3}{*}{NN} & \multirow{3}{*}{17.26} & \multirow{3}{*}{13.51} & 99.43\% & PPG, EDA, ST, & & & \\  & & & & & (79.83\%) & Food logs & & & \\ \cline{1-1}  & & & & & & (200 s) & & \\ \hline \multirow{3}{*}{This work} & \multirow{3}{*}{LightGBM} & \multirow{3}{*}{15.36} & \multirow{3}{*}{13.17} & 100\% & PPG & & & \\  & & & & (30 s) & & & \\ \cline{1-1}  & & & & & (30 s) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison with current work on estimating blood glucose. activity, bioimpedance, or skin temperature, as demonstrated by [PERSON] et al. [12] and [PERSON] et al. [5], the proposed model solely utilizes the PPG signal and chooses 18 features to build the model, thus simplifying the system. In contrast to [PERSON] et al. [7], whose deep learning approach had predictions in dangerous CEG zones, our model ensured all predictions were within safe zones. Compared to [PERSON] et al. 's [6] smartphone-based PPG approach, which exhibited lower predictive accuracy, our model provides better reliability with a broader detection range of 50-150 mg/dL. Studies like [PERSON] et al. [11], which used additional physiological inputs such as BMI and blood pressure, achieved lower accuracy and higher system complexity, underscoring the streamlined effectiveness of our approach. Unlike models requiring larger feature sets or additional user input, our approach combines metaheuristic algorithms and machine learning to balance accuracy, feature reduction, and simplicity. Furthermore, while many prior studies were limited by small sample sizes, our model was validated on a larger and more diverse dataset, enhancing its generalizability and applicability. By integrating metaheuristic algorithms with ML models, this study advances the field of non-invasive glucose monitoring. The streamlined IDA-LightGBM model balances accuracy, feature reduction, and simplicity, providing a practical solution for diabetes management. To the best of our knowledge, this is the first study to leverage metaheuristic optimization for BGL estimation using PPG signals, marking a significant step forward in non-invasive diabetes care.