###### Abstract
used neural networks to predict the mean group age from nine age groups of 22,433 subjects with 85% accuracy [24]. These results demonstrate the potential of using HRV features for machine learning-based automated age prediction. Notably, these studies on the automated prediction of age used precalculated HRV features as input data for machine learning models. This is a classical machine learning approach based on handcrafted features, which requires the extraction of predefined features from raw data, that is, HRV features from RRI data. Conventional feature engineering methods require the prior knowledge of human experts and additional data processing to select the best feature set. By contrast, end-to-end learning methods, such as deep neural networks (DNNs), present an alternative to the classical approach by learning and extracting representations from raw signals without requiring explicitly defined features [27; 28]. Deep neural network models, such as convolutional neural networks (CNNs) and long short-term memory (LSTM), have recently experienced a remarkable breakthrough, outperforming conventional methods in many applications, including image classification and natural language processing [27]. The use of RRI time-series data as physiological markers has been explored using a deep learning approach based on the observation that RRI data reflect autonomic cardiovascular regulation and the resulting cardiac rhythm. R-peak to R-peak interval data have been directly applied to DNNs without performing feature engineering to implement end-to-end learning in the detection of abnormal cardiac activity caused by medical conditions, such as arrhythmia [29; 30; 31; 32; 33; 34] and sleep apnea [35; 36; 37]. For example, [PERSON] et al. used RRI segment data with an LSTM model to distinguish between normal sinus rhythm (NSR) and atrial fibrillation (AFIB) [29]. [PERSON] et al. proposed a modified residual neural network (ResNet) to detect apnea segments in RRI data [35]. However, to the best of our knowledge, deep learning-based age prediction has rarely been performed using RRI data. A previous study demonstrated age prediction using RRIs with a deep learning architecture, comprising a CNN and LSTM [38]. In that study, the highest accuracy in classifying seven age groups of healthy subjects was 32.43%, indicating that automated age prediction based on RRI data using deep learning is challenging. Therefore, as a pilot study, we aimed to investigate the feasibility of using RRI data in conjunction with a deep learning approach as a biomarker for age prediction. Our main hypothesis was that an RRI-based deep learning model would offer similar or superior performance in age prediction compared with a conventional HRV feature-based model. We compared the proposed RRI model with the HRV model because previous studies have demonstrated that HRV features could be used to predict age based on a machine learning approach [22; 23; 24; 25; 26]. We focused on aging in a healthy population to build a baseline model representing normal aging. Because the baseline model is trained to learn healthy aging, we expect that a patient with age-related diseases would show an older estimated age than their chronological age. The difference between the baseline model's estimated age and chronological age would indicate the risk of premature aging and disease development. Therefore, it is important to develop an age estimation model based on only healthy subjects before we apply this method to a broader population that includes unhealthy subjects. To test our hypothesis, we used the RRI data collected from healthy subjects with a modified ResNet model to predict their age groups. We extracted the HRV features from the same RRI dataset to establish an HRV-based prediction model as a benchmark model. The RRI and HRV models were compared to examine whether the prediction was improved by implementing a deep learning approach.