Estimation of Length of Patient Stay (LOS) based on ML Algorithms


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Authors

  • Abdulkadir Atalan Gaziantep Islam Science and Technology University

DOI:

https://doi.org/10.59287/icriret.1401

Keywords:

Length of Stay, Machine Learning, Adaboost, Random Forest, Prediction

Abstract

In this study, Random Forest (RF) and Adabbost (AB) algorithms from machine learning models were used to estimate the length of stay (LOS) of patients treated in a hospital. Dependent and independent variable data from 6247 patients were used for the study. The developed ML model and the AB algorithm showed the best performance. AUC, CA, F1, Prec, Recall, and MCC values for the AB model were calculated as 0.999, 0.994, 0.994, 0.994, 0.994, and 0.991, respectively. AUC, CA, F1, Prec, Recall, and MCC values of the performance measurement values of the RF algorithm were calculated as 0.982, 0.897, 0.894, 0.900, 0.897, and 0.830, respectively. ML models were developed, a patient's LOS was calculated, and bed planning for hospital management was done efficiently with the present study.

Author Biography

Abdulkadir Atalan, Gaziantep Islam Science and Technology University

Industrial Engineering,  Gaziantep, Turkey

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Published

2023-08-29

How to Cite

Atalan, A. (2023). Estimation of Length of Patient Stay (LOS) based on ML Algorithms. International Conference on Recent and Innovative Results in Engineering and Technology, 209–214. https://doi.org/10.59287/icriret.1401