Rapid respiratory rate (RR) changes in older adults may indicate serious illness. Therefore, accurately estimating RR for cardiorespiratory fitness is essential. However, machine learning algorithm-related errors are unsuitable for medical decision-making processes because some data have a much larger sample size in the training set than in other sets. This difference in size refers to data imbalance. Therefore, we introduce a novel methodology combining bootstrap-based imbalanced feature generation (BIFG) with the Gaussian process for estimating RR and uncertainty, thereby addressing data imbalance. The sample difference between normal breathing (12–20 bpm), dyspnea ( \(\ge\) 20 bpm), and hypopnea (<8 bpm) indicates significant data imbalance, which can affect the learning of the machine learning algorithm. Thus, the normal breathing part with much data is well-trained. The dyspnea and hypopnea parts with relatively little data are not well-trained, and this data imbalance causes significant errors concerning the reference variables in the actual dyspnea and hypopnea data parts. Hence, we use the parametric bootstrap model generated by artificial feature curves to estimate RR and solve this problem. As a result, the non-parametric bootstrap approach drastically increased the number of artificial feature curves. The generated artificial feature curves are selectively utilized for the highly imbalanced parts. Therefore, BIFG can be efficiently trained to predict the complex nonlinear relationships between the feature vectors obtained from the photoplethysmography signals and the reference RR. The proposed methodology exhibits more accurate predictive performance and uncertainty. The mean absolute errors are 0.89 and 1.44 beats per minute for RR using the proposed BIFG based on the two data sets.
