Predicting the number of disease occurrence using recurrent neural network순환신경망을 이용한 질병발생건수 예측
- Other Titles
- 순환신경망을 이용한 질병발생건수 예측
- Authors
- Lee, Seunghyeon; Yeo, In-Kwon
- Issue Date
- Oct-2020
- Publisher
- KOREAN STATISTICAL SOC
- Keywords
- elderly patient medical data; weather data; GEE; RNN
- Citation
- KOREAN JOURNAL OF APPLIED STATISTICS, v.33, no.5, pp 627 - 637
- Pages
- 11
- Journal Title
- KOREAN JOURNAL OF APPLIED STATISTICS
- Volume
- 33
- Number
- 5
- Start Page
- 627
- End Page
- 637
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/1142
- DOI
- 10.5351/KJAS.2020.33.5.627
- ISSN
- 1225-066X
2383-5818
- Abstract
- In this paper, the 1.24 million elderly patient medical data (HIRA-APS-2014-0053) provided by the Health Insurance Review and Assessment Service and weather data are analyzed with generalized estimating equation (GEE) model and long short term memory (LSTM) based recurrent neural network (RNN) model to predict the number of disease occurrence. To this end, we estimate the patient's residence as the area of the served medical institution, and the local weather data and medical data were merged. The status of disease occurrence is divided into three categories(occurrence of disease of interest, occurrence of other disease, no occurrence) during a week. The probabilities of categories are estimated by the GEE model and the RNN model. The number of cases of categories are predicted by adding the probabilities of categories. The comparison result shows that predictions of RNN model are more accurate than that of GEE model.
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