## 5 Discussion
Therefore, to improve the accuracy of oxygen saturation predictions, it may be necessary to move beyond the conventional approach of using specific color channels and instead consider employing different color channels for each subject. However, this study did not utilize a more diverse dataset encompassing various demographics such as gender, age, and skin tone. This limitation could impact the generalizability of the research findings. For instance, certain gender, age, or skin tone groups may have different color channels that affect oxygen saturation. Future research should analyze the color channels contributing to oxygen saturation prediction in greater detail by utilizing more diverse datasets, thereby enhancing our understanding of the influences of features and improving the predictive power of the model across different environments. Furthermore, the database used in this study only included oxygen saturation values ranging from 85% to 100%, obtained by instructing participants to hold their breath. Therefore, models trained to predict oxygen saturation using this database may exhibit a tendency to predict lower oxygen saturation values than those used in training. To address this limitation, it is necessary to employ more diverse databases reflecting a wider range of oxygen saturation values. Considering that in the future, the International Organization for Standardization (ISO) will verify the accuracy of pulseoximeters through hypoxia-induced clinical trials that change oxygen saturation by 70 to 100%, the data needs to diversify the range of oxygen saturation changes. This will involve utilizing datasets with a more varied range of oxygen saturation changes for predicting oxygen saturation. Therefore, future research plans involve conducting studies to predict oxygen saturation using more diverse datasets that consider gender, age, skin color, and varied ranges of oxygen. This will involve analyzing the color channels contributing to oxygen saturation prediction and diversifying prediction models by grouping individuals with similar contributions from each color channel without creating individualized models. **Author Contributions:** Conceptualization, E.C.L. ; methodology, H.A.S. and C.L.S. ; data collection, H.A.S. ; software, H.A.S. and C.L.S. ; validation, H.A.S. ; formal analysis, H.A.S. and C.L.S. ; investigation, H.A.S. and C.L.S. ; writing--original draft preparation, H.A.S. and C.L.S. ; writing--review and editing, E.C.L. ; visualization, H.A.S. ; supervision, E.C.L. ; project administration, E.C.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This paper was supported by the Field-oriented Technology Development Project for Customs Administration through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT and Korea Customs Service (2022M31 IA1095155). **Institutional Review Board Statement:** Based on the 13-1-3 of the Enforcement Regulations of the Act on Bioethics and Safety of the Republic of Korea, ethical review and approval were waived (IRB-SMU-C-2023-1-008) for this study by Sangmyung University Institutional Review Board, because this study uses only simple contact measuring equipment or observation equipment that does not follow physical changes. **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. **Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. **Conflicts of Interest:** The authors declare no conflict of interest. Figure A2: SHAP values of the personalized model used in Figure 11 (all subjects). Figure 14: SHAP values for the training and testing data of a model trained with 10 s of signals as the input data (all subjects). Figure 15: SHAP values for the training and testing data of a model trained with 20 s of signals as the input data (all subjects).