## 1 Introduction  
Personalized healthcare has received worldwide attention due to technical improvement [1]. Both special groups (e.g., company or research center) and ordinary individuals are able to access wearable devices involving real-time monitoring sensors [2, 3, 4]. To be more specific, a smart watch (e.g., Apple watch) can be used to track the irregular pulse of a person, so that potential heart anomalies can be detected in real time [5]. In addition, a wearable tracker, such as a Fitbit, can be used to track the physical activity of an individual [3]. Given that most OECD countries have become aging societies and their lifestyle is rapidly changing towards single households [6, 7], the growth of the personalized healthcare market is not in question. There are two types of personalized healthcare in terms of point in time of treatment. Wearable devices used to monitor anomalies in health can be considered as proactive treatment. This is because real-time information can mitigate the reaction time of a first responder [8]. According to the personal health devices (PHDs) standard [9], proactive treatment can be illustrated as activities that allow people to conduct self-monitoring of health conditions and to share that information with healthcare professionals and other caregivers. Proactive treatment consists of two major types of devices: detecting anomalies using users' health data, and curing discomfort. An example of the former is the Advanced care and alert portable telemedical MONitor (AMON) [10], which continuously collects vital signs and alerts the medical center of anomalies; an example of the latter is the Quell, by Neuro Metrix Inc., which relieves chronic leg pain via transcutaneous electrical nerve stimulation [11]. On the other hand, preventive treatment is associated with maintaining the individual's health [12]. The most popular approaches in this category are home training and dietary therapy [13]. In fact, under the COVID-19 pandemic, home training and dietary therapy are becoming more common and popular in many countries, including the U.S., Germany, France, Japan, China, and South Korea [14]. According to _Fortune_, the market size of home fitness equipment in the U.S. in 2020 was about USD 3.55 billion, and by 2028, is expected to grow to USD 14.74 billion [15]. Similarly, the market size of dietary supplements in 2020 was about USD 61.20 billion, and by 2028 is expected to be USD 128.64 billion. Generally speaking, this is known as a more effective approach in terms of cost and fatality compared with proactive treatment [16]. In preventive treatment, it is critical to understand the correlation between anthropometric variables and diseases, such as diabetes, hypertension, dyslipidemia, and coronary artery disease [17]. Once the correlation is analyzed, it can be used to build a prediction model of human diseases. This implies that diseases can be prevented by changing anthropometric variables in terms of decreasing waist circumference, waist-hip ratio, and body mass index (BMI). Additionally, a number of studies for predicting disease were conducted. [PERSON] et al. [18] proposed cardiovascular disease (CVD) prediction models. The prediction model was based on the deep learning method (RNN\(-\)LSTM), which is widely used to analyze time-series datasets. This technique improved the prediction accuracy compared to conventional Cox proportional hazard regression models [19]. In addition, various companies have participated in developing and commercializing services that can diagnose disease and health abnormalities by analyzing users' health record data; e.g., Apple received FDA approval for a deep learning algorithm that detects atrial fibrillation using their smartwatch [4].