## 6 Conclusions  
The proposed method improved the accuracy and stability of BP and CI estimations using the GFSDP algorithm. This algorithm measures uncertainties, such as CIs, error standard deviation, and bias for cuffless SBP and DBP estimations. Features with high weights for feature selection were selected by applying the F-test and RNCA methods. The AIC algorithm was then used to determine the optimal set of features. In addition, despite the small sample size for each patient, the CIs were presented using the bootstrap method to represent the uncertainty in cuffless BP estimation. The proposed GFSDP is the only one that provides BP and CI assessment concurrently for cuffless BP measurements. Furthermore, the proposed GFSDP algorithm can be designed as a framework for improving healthcare monitoring and optimizing clinical decision support systems. Although public datasets were used in the experiments, the scope of this study is limited by the small sample size for each patient. This is because the CIs of all algorithms representing the uncertainty are extensive. The CIs generated by the GP algorithm are also wide owing to the wide SDE of the ME. Future studies should improve the performance of the GFSDP algorithm by reducing the SDE of the ME. Moreover, the complexity of the GFSDP algorithm should be improved for BP and CI estimations.  
Conceptualization, S.L.; methodology, S.L. and G.L.; software, S.L.; validation, S.L., C.-H.S. and G.P.J.; formal analysis, C.-H.S.; investigation, C.-H.S.; resources, A.P.S.; data curation, G.P.J.; writing--original draft preparation, S.L.; writing--review and editing, G.P.J. and G.L.; visualization, A.P.S.; supervision, C.-H.S.; project administration, G.P.J. and A.P.S.; funding acquisition, C.-H.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by Korea government (MSIT) (No. 2020R1A2C1010405)  
Please refer to suggested Data Availability Statements in section \"MDPI Research Data Policies\" at [[https://archive.ics.uci.edu/ml/datasets/Cuff-Less+Blood+Pressure+Estimation](https://archive.ics.uci.edu/ml/datasets/Cuff-Less+Blood+Pressure+Estimation)]([https://archive.ics.uci.edu/ml/datasets/Cuff-Less+Blood+Pressure+Estimation](https://archive.ics.uci.edu/ml/datasets/Cuff-Less+Blood+Pressure+Estimation)), accessed on 10 March 2022. Upon a reasonable request, the corresponding author can offer a partial code for the study upon completion of all projects.  
The present research has been conducted by the Research Grant of Kwangwoon University in 2021.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BPs & Blood pressures \\ CIs & Confidence intervals \\ CVD & Cardiovascular disease \\ ML & Machine learning \\ ECG & Electrocardiogram \\ PPG & Photoplethysmography \\ PTT & Pulse transit time \\ PWV & Pulse wave velocity \\ MLR & Multiple linear regression \\ GP & Gaussian process \\ SVM & Support vector machine \\ ANN & Artificial neural networks \\ LSTM & Long short-term memory \\ RNCA & Robust neighbor component analysis \\ NCA & Neighbor component analysis \\ AIC & Akaike's information criterion \\ ABP & Arterial blood pressure \\ SBP & Systolic blood pressure \\ DBP & Diastolic blood pressure \\ BHS & British hypertension protocol \\ ME & Mean error \\ SDE & Standard deviation of the error \\ MAE & Mean absolute error \\ AAMI & Association for the Advancement of Medical Instrumentation \\ \end{tabular}