## 1 Introduction  
As quality of life has increased, interest in health and illnesses has also increased [1]. Moreover, as the healthcare industry pursues fundamental changes in medical services through the technologies of the Fourth Industrial Revolution, such as big data, artificial intelligence (AI), and the internet of things (IoT), its paradigm is shifting from a diagnosis-and-treatment-centered model to a prevention-and-management-centered model [2]. This is a shift from the traditional healthcare paradigm in which people go to hospitals when they feel sick to one in which people predict and prevent illnesses using information technology (IT) [3].  
Recently, the utilization of IT has become essential in various fields, and services that create new value by using data collected with IT are emerging all over the world. Therefore, big data is believed to be the innovative factor that will create new national values and solve various countries' fundamental problems [4, 5]. In Korea, public data from various fields are provided to private companies and researchers regardless of the original purpose of the data collection, and they are typically available through public data portal sites [6]. The provided data are thus accessible for anyone to collect and analyze. As part of a pilot project for the usage of medical big data, the Korean government has utilized the Korean Meteorological Administration's (KMA) health meteorological index and developed an early warning service for drug safety [5, 7]. The utilization of healthcare data has been increasing since it was made public. If this rapidly growing collection of healthcare-related data is utilized well, innovation in medical services is possible, such as complete diagnosis customized for each patient and illness prevention services [2]. The following examples of innovation in medical services are expected: (1) Complete diagnosis customized for each patient [8]; (2) Illness prevention real-time service: Real-time digital disease surveillance data and public data sharing could shape predictions about future epidemics [9]; (3) Disease prediction using wearable devices: Sensor data can help analyze trends in seasonal respiratory infections, such as influenza [10].  
This study aimed to analyze healthcare data using data mining. Data mining refers to a technique that reveals the underlying useful knowledge in big data [11] and is effective at deriving hidden patterns and discovering descriptive, predictive, and understandable models [12; 13]. A key example is machine learning. Machine learning has advantages in such tasks as diagnosis, classification, and survival prediction. It has a particular advantage in the healthcare field, as it can analyze various data types and provide predictions regarding daily illness risks, diagnosis, prognosis, and appropriate treatments [14]. Moreover, machine learning may be able to serve as a treatment option that can help people make better decisions in consideration of their healthcare needs and diagnosis [15]. Therefore, the expectation in this study is that an increase in the healthy lifespan and a reduction in medical expenses of humans can be obtained if healthcare data are analyzed with machine learning.  
The purpose of this study was to analyze the illnesses that most commonly occur in children and older adults, two socially vulnerable groups that need more protection than other age groups [16], and to provide the degree of daily risk for illnesses based on the number of patients suffering from them in each region. In this process, the optimal machine-learning model was selected after extensive comparison, the optimal hyperparameter was derived through GridSearchCV, and both were verified based on their R\({}^{2}\) value. This study's strengths are that the degree of daily risk for each illness for each age group can be provided, and the most common illness for each age group can be determined. As a matter unrelated to actual diagnosis by a doctor, this is expected to raise awareness of and prevent illnesses based on climate data.