The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
권호 표지
DOI QR코드

DOI QR Code

Time Series Data Processing Deep Learning system for Prediction of Hospital Outpatient Number

병원 외래환자수의 예측을 위한 시계열 데이터처리 딥러닝 시스템

  • Jo, Jun-Mo (Dept. Electronic Engineering, TongMyong University)
  • Received : 2021.02.16
  • Accepted : 2021.04.17
  • Published : 2021.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

The advent of the Deep Learning has applied to many industrial and general applications having an impact on our lives these days. Certain type of machine learning model is needed to be designed for a specific problem of field. Recently, there are many instances to solve the various COVID-19 related problems using deep learning model. Therefore, in this paper, a deep learning model for predicting number of outpatients of a hospital in advance is suggested. The suggested deep learning model is designed by using the Keras in Jupyter Notebook. The prediction result is being analyzed with the real data in graph, as well as the loss rate with some validation data to verify either for the underfitting or the overfitting.

딥러닝 기술의 도래로 인하여 수많은 산업과 일반적인 응용에 적용됨으로써 우리의 생활에 큰 영향을 발휘하고 있다. 특정한 분야의 문제를 해결하기 위해서는 그 문제에 적합한 딥러닝 모델을 작성해야 한다. 근래에는 COVID-19 사태로 인하여 다양한 문제들을 딥러닝으로 해결하고자 하는 사례들이 늘고 있다. 이러한 일환으로 본 논문에서는 갑자기 급증할 수 있는 병원의 외래환자들을 미리 예측을 위한 시계열의 딥러닝 모델을 제시하고자 한다. 제시하는 딥러닝 모델은 주피터 노트북에서 케라스로 작성하였다. 예측결과는 실제 데이터와 그래프로 비교하며 유효성 데이터를 활용하여 과소적합과 과대적합의 여부를 손실률로 분석할 수 있도록 하였다.

Keywords

References

  1. R. Sathya and A. Annamma, "Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification," IJARAI, vol. 2, no. 2, 2013, pp. 34-38.
  2. R. Sathya and A. Abraham, "Unsupervised Control Paradigm for Performance Evaluation," International Journal of Computer Application, vol. 44, no. 20, 2012, pp. 27-31. https://doi.org/10.5120/6380-8850
  3. X. C. Yin, X. Yin, K. Huang, and H. W. Hao, "Robust text detection in natural scene images," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 36, no. 5, 2014, pp. 970-983. https://doi.org/10.1109/TPAMI.2013.182
  4. H. Lee and S. Oh, "LSTM-based Deep Learning for Time Series Forecasting: The Case of Corporate Credit Score Prediction," Korea Association of Information Systems, vol. 29, no. 1, 2020, pp. 241-265.
  5. C. Im, "EEG Dimensional Reduction with Stack AutoEncoder for Emotional Recognition using LSTM/RNN," J. of the Korea Institute of Electronic Communication Science, vol. 15, no. 4, 2020, pp. 717-724. https://doi.org/10.13067/JKIECS.2020.15.4.717
  6. C. Neocleous and C. Schizas, "Artificial Neural Network Learning: A Comparative, Methods and Applications of Artificial Intelligence," Hellenic Conference on Artificial Intelligence SETN, Springer, Thessaloniki, Greece, 2002, pp. 300-313.
  7. Y. Milad, Y. Moslem, F. Masood, and F. Flavio, "Patient visit forecasting in an emergency department using a deep neural network approach," The international journal of cybernetics, vol. 49, issue. 12, 2019, pp. 2335-2348.
  8. M. Kim, "A Study on the Sports Rehabilitation Treatment for the Intellectual Disabilities using deep learning," J. of the Korea Institute of Electronic Communication Science, vol. 15, no. 4, 2020, pp. 725-732. https://doi.org/10.13067/JKIECS.2020.15.4.725
  9. C. Jung, R. Jang, D. Nyang, and K. Lee " A Study of User Behavior Recognition-Based PIN Entry Using Machine Learning Technique," Korea Information Processing Society review, computer and communication systems, vol. 7, no. 5, 2018, pp. 127-136.