Predicting Length of Stay in Intensive Care Units after Cardiac Surgery: Comparison of Artificial Neural Networks and Adaptive Neuro-fuzzy System

Healthcare Informatics Research
Hamidreza MaharlouRamin Ravangard

Abstract

Accurate prediction of patients' length of stay is highly important. This study compared the performance of artificial neural network and adaptive neuro-fuzzy system algorithms to predict patients' length of stay in intensive care units (ICU) after cardiac surgery. A cross-sectional, analytical, and applied study was conducted. The required data were collected from 311 cardiac patients admitted to intensive care units after surgery at three hospitals of Shiraz, Iran, through a non-random convenience sampling method during the second quarter of 2016. Following the initial processing of influential factors, models were created and evaluated. The results showed that the adaptive neuro-fuzzy algorithm (with mean squared error [MSE] = 7 and R = 0.88) resulted in the creation of a more precise model than the artificial neural network (with MSE = 21 and R = 0.60). The adaptive neuro-fuzzy algorithm produces a more accurate model as it applies both the capabilities of a neural network architecture and experts' knowledge as a hybrid algorithm. It identifies nonlinear components, yielding remarkable results for prediction the length of stay, which is a useful calculation output to support ICU management, enabling higher quality of admini...Continue Reading

References

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Citations

Sep 30, 2020·Journal of the American Heart Association·Louise Y SunThierry G Mesana
Apr 2, 2019·Annals of Vascular Diseases·Ali Kordzadeh, Shabnam Sadeghi Esfahlani
Mar 31, 2021·The Journal of Surgical Research·Austin J TrianaVikram Tiwari

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Methods Mentioned

BETA
coronary artery bypass

Software Mentioned

MATLB
SPSS
MATLAB
ANFIS

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