## 1 Introduction
The proposed hybrid feature selection and decision based on the GP algorithm can provide a means for distinguishing between estimation errors (statistical variance of estimates) and changes in estimates owing to physiological variability [5; 7]. This study obtained CIs using a bootstrap algorithm [10; 35] to determine the uncertainty (physiological variability) in cuffless BP estimation. Specifying CIs for cuffless measurements is beneficial because CIs measurement are necessary for estimating BP. If the BP measurement's CIs are too broad, healthcare workers may misjudge a patient's health status. Therefore, establishing CIs based on accurate BP estimates allows for more accurate and faster meaningful determinations of BP measurement CIs [9]. However, studies on determining the uncertainty of physiological measurements [4] using oscillometric BP signals [5; 6; 7; 8] are limited. Therefore, a new method for evaluating and representing the uncertainty in BP measurement should be developed by providing an estimated range for cuffless BP measurements. Consequently, repeatable, irregular, and broad CIs based on aggregated statistical data can provide patients, clinicians, and families a warning system for BPs outside the normal range [5; 9; 10]. Another problem in improving the performance of ML algorithms is the choice of features to replace the original ones and using them as input data. Feature selection is an essential part of the learning algorithm's performance; it selects a subset of features with higher weights for the response variable and eliminates duplicate features [36; 37]. Thus, the proposed methodology uses a hybrid F-test [38] and robust neighbor component analysis (RNCA) [39] to select weighted features from the original features. The best feature set is selected using [PERSON]'s information criterion (AIC) [40] based on the maximum likelihood from the GP algorithm [31]. First, the F-test is used to acquire weighted features to compute a feature's within- and between-group variance ratios [38]. Second, the weighted features are obtained using RNCA [39], which selects the high-weighted features from the original features. The importance of the BP estimation performance can be corrected by repeating the estimation experiment several times. Therefore, this study proposes an adaptive AIC of automatically calibrated likelihoods based on the GP algorithm to determine the best feature subset as a model selection problem, yielding an excellent performance with negligible computational overhead after calibration. This study provides uncertainty for cuffless BP measurements and introduces a method for reducing the error of BP estimates based on PPG and ECG signals. As previously mentioned, the proposed study estimates the exact BPs and CIs concurrently, representing the uncertainty of cuffless BP estimation. To the best of the authors' knowledge, this study is the first to propose a GP-based feature selection and decision process (GFSDP) algorithm to simultaneously estimate cuffless BPs and CIs. Although some CI studies on conventional oscillometric BP estimation methods have been performed [5; 6; 7; 8], studies on CI estimation in cuffless BP measurements are limited, as summarized in Table 1. The contributions of the study to BP and CI estimations are as follows:  
* CIs are estimated using a bootstrap based on the GP algorithm to express uncertainty in cuffless BP estimation. * The proposed methodology uses a hybrid F-test and RNCA to select the weighted features among the original features. * An adaptive AIC of automatically calibrated likelihoods is proposed based on the GP algorithm to determine the best feature subset as a model selection problem.