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Prediction of Diagnosis-Related Groups for Appendectomy Patients Using C4.5 and Neural Network
Due to the increasing cost of health insurance, for decades, many countries have endeavored to constrain the cost of insurance by utilizing a DRG payment system. In most cases, under the DRG payment system, hospitals cannot exactly know which DRG code inpatients are until they are discharged. This p...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10253080/ https://www.ncbi.nlm.nih.gov/pubmed/37297737 http://dx.doi.org/10.3390/healthcare11111598 |
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author | Chiang, Yi-Cheng Hsieh, Yin-Chia Lu, Long-Chuan Ou, Shu-Yi |
author_facet | Chiang, Yi-Cheng Hsieh, Yin-Chia Lu, Long-Chuan Ou, Shu-Yi |
author_sort | Chiang, Yi-Cheng |
collection | PubMed |
description | Due to the increasing cost of health insurance, for decades, many countries have endeavored to constrain the cost of insurance by utilizing a DRG payment system. In most cases, under the DRG payment system, hospitals cannot exactly know which DRG code inpatients are until they are discharged. This paper focuses on the prediction of what DRG code appendectomy patients will be classified with when they are admitted to hospital. We utilize two models (or classifiers) constructed using the C4.5 algorithm and back-propagation neural network (BPN). We conducted experiments with the data collected from two hospitals. The results show that the accuracies of these two classification models can be up to 97.84% and 98.70%, respectively. According to the predicted DRG code, hospitals can effectively arrange medical resources with certainty, then, in turn, improve the quality of the medical care patients receive. |
format | Online Article Text |
id | pubmed-10253080 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102530802023-06-10 Prediction of Diagnosis-Related Groups for Appendectomy Patients Using C4.5 and Neural Network Chiang, Yi-Cheng Hsieh, Yin-Chia Lu, Long-Chuan Ou, Shu-Yi Healthcare (Basel) Article Due to the increasing cost of health insurance, for decades, many countries have endeavored to constrain the cost of insurance by utilizing a DRG payment system. In most cases, under the DRG payment system, hospitals cannot exactly know which DRG code inpatients are until they are discharged. This paper focuses on the prediction of what DRG code appendectomy patients will be classified with when they are admitted to hospital. We utilize two models (or classifiers) constructed using the C4.5 algorithm and back-propagation neural network (BPN). We conducted experiments with the data collected from two hospitals. The results show that the accuracies of these two classification models can be up to 97.84% and 98.70%, respectively. According to the predicted DRG code, hospitals can effectively arrange medical resources with certainty, then, in turn, improve the quality of the medical care patients receive. MDPI 2023-05-30 /pmc/articles/PMC10253080/ /pubmed/37297737 http://dx.doi.org/10.3390/healthcare11111598 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chiang, Yi-Cheng Hsieh, Yin-Chia Lu, Long-Chuan Ou, Shu-Yi Prediction of Diagnosis-Related Groups for Appendectomy Patients Using C4.5 and Neural Network |
title | Prediction of Diagnosis-Related Groups for Appendectomy Patients Using C4.5 and Neural Network |
title_full | Prediction of Diagnosis-Related Groups for Appendectomy Patients Using C4.5 and Neural Network |
title_fullStr | Prediction of Diagnosis-Related Groups for Appendectomy Patients Using C4.5 and Neural Network |
title_full_unstemmed | Prediction of Diagnosis-Related Groups for Appendectomy Patients Using C4.5 and Neural Network |
title_short | Prediction of Diagnosis-Related Groups for Appendectomy Patients Using C4.5 and Neural Network |
title_sort | prediction of diagnosis-related groups for appendectomy patients using c4.5 and neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10253080/ https://www.ncbi.nlm.nih.gov/pubmed/37297737 http://dx.doi.org/10.3390/healthcare11111598 |
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