A comparative study of neuro-fuzzy and neural network models in predicting length of stay in university hospital

Various AI models can effectively predict LOS for patients in different hospital units. Accurate LOS predictions can help health managers allocate resources more equitably across units.

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Publication Details

Journal
BMC Health Services Research
Published
2025-03-27
DOI
https://doi.org/10.1186/s12913-025-12623-x
Primary Topic
Advanced Data Processing Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

A comparative study of neuro-fuzzy and neural network models in predicting length of stay in university hospital

Elif DİKMETAŞ YARDAN, Birgül YABANA KİREMİT
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BMC Health Services Research
Advanced Data Processing Techniques
article

A comparative study of neuro-fuzzy and neural network models in predicting length of stay in university hospital

Elif DİKMETAŞ YARDAN, Birgül YABANA KİREMİT
article en

Abstract

Various AI models can effectively predict LOS for patients in different hospital units. Accurate LOS predictions can help health managers allocate resources more equitably across units.

BMC Health Services ResearchVol. 25(1)
Ondokuz Mayıs University (TR)
Openalex Percentile: Top 94%
Advanced Data Processing Techniques
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A comparative study of neuro-fuzzy and neural network models in predicting length of stay in university hospital — Elif DİKMETAŞ YARDAN, Birgül YABANA KİREMİT · BMC Health Services Research (2025) | TGRS Research Map | TGRS