## 3 Background
### Binary Harris Hawks Optimizer (BHHO)
## 6 Conclusions and Future Work

This study introduced a novel approach for non-invasive blood glucose level (BGL) estimation utilizing HRV features derived from PPG signals. By integrating advanced preprocessing techniques, metaheuristic-based feature selection, and machine learning (ML) models, the proposed approach demonstrated high accuracy and clinical significance. The IDA-LightGBM model was identified as the most effective combination, achieving a mean absolute error (MAE) of 13.17 mg/dL and a root mean squared error (RMSE) of 15.36 mg/dL while selecting only 18 features. It achieved 94.74% of predictions within Clarke Error Grid (CEG) Zone A, with the remaining predictions falling into Zone B, and none in the dangerous Zones C, D, or E. Furthermore, in comparison to other monitoring systems that may require the use of multi-sensing or a greater number of features, this approach offers a practical and broader detection range of 50-150 mg/dL utilizing solely PPG signals. Despite its promising findings, the study's results are limited by the relatively small sample size of 52 subjects, as well as the examined glucose range that excludes conditions of severe hyperglycemia or hypoglycemia. Expanding this work to include more diverse populations and more comprehensive glucose levels would enhance the validity and reliability of the model. Future work will involve testing the model in realistic settings, for example, an application that tracks glucose levels through wearable technology, and exploring other algorithms to improve the performance of the system. These advancements can enhance the validity and practicality of non-invasive glucose monitoring and therefore enhance diabetes management. **Supplementary Materials:** The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/a18020095/s1](https://www.mdpi.com/article/10.3390/a18020095/s1)]([https://www.mdpi.com/article/10.3390/a18020095/s1](https://www.mdpi.com/article/10.3390/a18020095/s1)), Table S1: Heart rate variability (HRV) metrics computed in this study [59]. **Author Contributions:** Conceptualization, S.A. and M.A.A.-B. ; methodology, S.A.; software, S.A.; validation, S.A. and M.A. ; formal analysis, M.A.A.-B. ; investigation, M.A.A.-B. ; resources, M.A. ; data curation, M.A. ; writing--original draft preparation, S.A.; writing--review and editing, S.A. and M.A.A.-B. ; visualization, M.A. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by Ajman University grant number 2024-IRG-ENIT-16, and the APC was funded by Ajman University. **Data Availability Statement:** The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author(s). **Conflicts of Interest:** The authors declare no conflict of interest. ## References

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