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



Maharlou H^{1, 2} ; Niakan Kalhori SR³ ; Shahbazi S⁴ ; Ravangard R^{1, 5} Show Affiliations
Source: Healthcare Informatics Research Published:2018

Abstract

Objectives: 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. Methods: 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. Results: 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). Conclusions: 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 administration and cost reduction. © 2018 The Korean Society of Medical Informatics.