# EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms
## 4 Conclusions  
In this study, we investigated the feasibility of noninvasive blood glucose monitoring through the analysis of PPG signals, utilizing a diverse array of features. This study highlighted the importance of intrinsic mode function (IMF)-based features and ratio-based features derived from red, green, and blue wavelength signals. Combining the PPG waveform-based top 20 features and IMF-based top 30 features significantly improved the overall prediction accuracy of the model, indicating the complementary nature of the two feature sets. The results show that the quality and type of features (top 50 features based on both PPG waveform and IMFs) play an important role in model performance. After selecting those 50 features, the Pearson's r values for RF, XGB, LightGBM, CatBoost were 0.94, 0.95, 0.93, and 0.96, respectively. Regardless of whether PPG waveform-based feature selection or IMFs-based feature selection was applied, CatBoost consistently outperformed the XGBoost, LightGBM, and RF algorithm in this specific application.  
Our findings in this study suggest that the proposed noninvasive blood glucose measurement system has the potential to provide accurate and reliable measurements of blood glucose levels, which may have important clinical implications for patients with diabetes. In the future, we plan to improve the performance of the proposed method by collaborating with medical institutions to acquire additional clinical data and improve and expand the proposed method through deep learning.  
S.S.: conceptualization, methodology, software, writing--original draft preparation, validation. M.S.T.: methodology, software, review. T.-H.K.: methodology, software, review. K.-D.K.: conceptualization, methodology, writing--original draft preparation, supervision, funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation (NRF) of Korea funded by the Ministry of Science and ICT (2022R1A5A7000765) and was also supported by Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education (NRF-2022R1A2C2010298).  
All protocols and procedures in this study were approved by the Institutional Review Board (IRB) of Kookmin University, Seoul, Korea (approval date: 17 July 2020). The procedures followed the Helsinki Declaration of 1975, as revised in 2008. All human participants agreed in advance to participate and share data for academic research purposes. The IRB protocol number is: KMU-202006-HR-237.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** We have created our own dataset for this study. Since further research is underway, we are unable to publish the dataset at present.  
**Conflicts of Interest:** The authors declare that they have no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the publication of the results.