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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Predicting Number of Hospital Appointments When No Data Is Available

Author 1: Harold Caceres
Author 2: Nelson Fuentes
Author 3: Julio Aguilar
Author 4: Cesar Baluarte
Author 5: Karim Guevara
Author 6: Eveling Castro-Gutierrez
Author 7: Omar U. Florez

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110681

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 6, 2020.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Usually, in a hospital, the data generated by each department or section is treated in isolation, believing that there is no relationship between them. It is thought that while one department is in high demand, it can not influence that another may have the same demand or not have any demand. In this paper, we question this approach by considering information from departments as components of a large system in the hospital. Thus, we present an algorithm to predict the appointments of departments when data is not available using data from other departments. This algorithm uses a model based on multiple linear regression using a correlation matrix to measure the rela-tionship between the departments with different time windows. After running our algorithm for different time windows and departments, we experimentally find that while we increase the extension of a time window and learn dependencies in the data, its corresponding precision decreases. Indeed, a month of data is the minimum sweet spot to leverage information from other departments and still provide accurate predictions. These results are important to develop per-department health policies under limited data, an interesting problem that we plan to investigate in future works.

Keywords: Multi linear Regression; hospital appointments; ma-chine learning; correlation matrix

Harold Caceres, Nelson Fuentes, Julio Aguilar, Cesar Baluarte, Karim Guevara, Eveling Castro-Gutierrez and Omar U. Florez, “Predicting Number of Hospital Appointments When No Data Is Available” International Journal of Advanced Computer Science and Applications(IJACSA), 11(6), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110681

@article{Caceres2020,
title = {Predicting Number of Hospital Appointments When No Data Is Available},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110681},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110681},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {6},
author = {Harold Caceres and Nelson Fuentes and Julio Aguilar and Cesar Baluarte and Karim Guevara and Eveling Castro-Gutierrez and Omar U. Florez}
}


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