## 5 Conclusions  
In this study, we explored the application of complex network technology in the non-invasive detection of Hb, designed a 'classification-regression stacking' model framework based on multi-sample weighted undirected graph clustering features and visibility \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Equipment Type** & \begin{tabular}{c} **Measuring Range** \\ **(g/L)** \\ \end{tabular} & \begin{tabular}{c} **Relative Error** \\ **(g/L)** \\ \end{tabular} & \
\begin{tabular}{c} **Confidence Upper** \\ **Interval (95\%)** \\ \end{tabular} &
\begin{tabular}{c} **Confidence Lower** \\ **Interval (95\%)** \\ \end{tabular} \\ \hline \hline Masimo Rad-67 & 80–170 & - & 20.7 & \(-\)18.2 \\ NBM200 & 110–170 & 9.9 & - & - \\ Masimo Pronto & 60–180 & 10 & - & - \\ Our Equipment & 110–170 & 8.21 & 18.7 & \(-\)15.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of common instruments in the market. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Research** & \begin{tabular}{c} **Input** \\ **Features** \\ \end{tabular} & **Model Use** & **Subject** & \begin{tabular}{c} **MAE** \\ **(g/L)** \\ \end{tabular} & **RMSE** & **R2 (0–1)** \\ \hline [PERSON], [PERSON], [PERSON] & MW-PPG & Stacked & 1583 & - & 13.53 & - \\ et al., 2019 [15] & (4 channel) & Regressor & 47 & 7.3 & 9.5 & 0.53 \\ \hline [PERSON], [PERSON], [PERSON] & PPG & Linear & 47 & 7.3 & 9.5 & 0.53 \\ S 2020 [37] & 7 features & Regression & 1005 & - & 14.7 & - \\ [PERSON], [PERSON], & MW-PPG & Stacked & 1005 & - & 14.7 & - \\ [PERSON], [PERSON] et al., 2021 [38] & (4 channel) & Regressor & 4512 & 8.3 & - & - \\ \hline [PERSON], [PERSON], [PERSON] et al., 2022 [39] & Image & \
\begin{tabular}{c} ASModel \\ _UWF_ \\ \end{tabular} & 4512 & 8.3 & - & - \\ \hline [PERSON], [PERSON], [PERSON], & MW-PPG & CNN & 170 & 13.6 & - & 0.43 \\ et al., 2023 [40] & (6 channel) & ResNet101 & 1244 & 11.9 & 14.8 & 0.12 \\ \hline [PERSON], [PERSON], [PERSON] et al., 2024 [5] & Image &
\begin{tabular}{c} ASModel \\ _UWF_ \\ \end{tabular} & 58 & 7.56 & 10.22 & 0.53 \\ \hline
**This work** &
\begin{tabular}{c} MW-PPG \\ **(4 channel)** \\ \end{tabular} & SVM-LGBM & 152 & 6.7 & 8.21 & 0.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of results from another research. features, and developed a Hb prediction system based on this model. Based on the comparison with previous studies and conventional instruments available on the market, it can be concluded that the present study has improved the accuracy of PPG signals for non-invasive Hb detection in general. However, this study is limited by the hardware equipment and experimental environment, as well as the limitations of data acquisition. It is difficult to achieve a wider range of acquisition, and our system can only be used as a basis for experimentation and research. In the future, we can continue to increase the collection of clinical data on MW-PPG signals and increase the number of samples of moderate anemia and severe anemia to obtain a wider prediction accuracy and prediction range; plateau anemia, thalassemia, and other special types of anemia may also correlate with MW-PPG signals. In terms of hardware improvement, this study can investigate the detection of methemoglobin and hemoglobin monoxide by increasing the wavelength of low frequency bands, such as 500-600 nm; and at the same time, it can reduce the size of the probe by increasing the degree of integration, such as making it into a watch to make it more convenient to carry. The roadmap for future improvements is shown in Figure 14, and these are the directions in which the study can advance to obtain more comprehensive conclusions and explanations. Figure 14: This figure illustrates the roadmap for future improvements in this study. **Author Contributions:** Methodology, software, L.L. ; Data curation, Z.W. ; Investigation, X.Z. Writing - review & editing, Y.L. ; Project administration, Y.Z. and Y.L. ; Funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.