## 5 Conclusions  
In this study, an approach to provide customized daily disease risk for children and the older adults using sample medical data of one million patients provided by the National \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(p\)**-Value** & **-0.2** & **0.2–0.4** & **0.4–0.6** & **0.6–0.8** & **0.8–** \\ \hline \(Z\) & \(\ldots-0.83\) & \(-0.83\)–\(\ldots-0.25\) & \(-0.25\)–\(0.25\) & 0.25–0.83 & 0.83– \\ Level & Very low & Low & Normal & High & Very high \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Cutoff standard and definition of risk of illness. Figure 1: Application. Health Insurance and weather environment and atmospheric information provided by the Korea Meteorological Administration (KMA) and public data portal API was proposed. Existing studies and systems have performed comprehensive analyses for all age groups without dividing the age groups. This study overcomes that limitation. By comparing the predictive R\({}^{2}\) of the three models, XGBoost was selected as the final disease risk prediction model. When R\({}^{2}\) was greater than 0.65, it was defined as a significant analysis. For children, it can provide four illness risk: acute bronchitis, the common cold, rhinitis and tonsillitis, and inner ear inflammation. Additionally, for older adults, it can provide six illness risk: high blood pressure and heart disease, the common cold, esophageal inflammation and gastritis, acute bronchitis, eczema and dermatitis, and chronic bronchitis. This study has the following limitations. First, the existing system of the KMA provides a health meteorological index for each city and state. However, this study also has limitations, as the medical data utilized in this study only provide information on province and metropolitan city subdivisions. Second, there is a limitation in not being able to analyze which factors have a great influence on specific diseases in specific seasons and days of the week. Lastly, the data used in this study are data up to 2016, the most recent among currently available data, and there is a limitation that there may be differences from the current situation. Therefore, if the latest data are updated later, it can be expected to be predictable according to the current situation. In future research, it is expected that it can become an interpretable model if a method of identifying which data affect disease for disease prediction is added. For this, the importance of features should be analyzed using techniques such as additive explanation (SHAP) [37] and permutation importance [38]. Although treatment can only be provided by a professional, illnesses may be prevented by heightened awareness of illnesses through the daily illness risks for each age group presented by this study based on collectible meteorological and atmospheric environment-related information. Conceptualization, M.K. ; methodology, M.K. ; formal analysis, M.K., J.J. and S.J. ; writing--original draft preparation, M.K. ; writing--review and editing, J.J., S.J. and S.Y. ; supervision, S.Y. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea (NRF-2020R1A2C2010471). **Data Availability Statement:** For weather data, we used the neighborhood forecast inquiry service API, and for atmospheric data, the Korea Environment Corporation Air Korea air pollution information API was used. Weather data and atmospheric data can be provided by using the API after applying for data utilization on the public data portal and obtaining approval ([[https://www.data.go.kr/index.do](https://www.data.go.kr/index.do)]([https://www.data.go.kr/index.do](https://www.data.go.kr/index.do)), accessed on 24 May 2022). And as for the medical treatment data used as the dependent variable, the National Health Insurance Corporation published the medical treatment history sample data for 1 million people per year until 2016.