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

Abstract

Background: The time a patient spends in the hospital from admission to discharge is known as the length of stay (LOS). Predicting LOS is crucial for enhancing patient care, managing hospital resources, and optimizing the use of patient beds. Therefore, this study aimed to predict the LOS for patients hospitalized in various clinics using different artificial intelligence (AI) models.

Methods: The study analyzed 162,140 hospitalized patients aged 18 and older at various clinics of a university hospital in northern Türkiye from 2012 to 2020. Three soft computing methods-Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Multiple Linear Regression Analysis (MLR)-were employed to estimate LOS using inputs such as medical and imaging services (number of CT, USG, ECG, hemogram tests, medical biochemistry, and number of direct x-rays), demographic, and diagnostic data (patients' age, sex, season of hospitalization, type of hospitalization, diagnosis, and second diagnosis). The LOS predictions utilized single and double-hidden layer ANNs with various training algorithms (Levenberg-Marquardt-LM, Bayesian Regularization-BR and Scaled Conjugate Gradient-SCG) and activation functions (tangent-sigmoid, purelin), ANFIS with Grid Partitioning (ANFIS-GP), and MLR. Model performance was evaluated using the Coefficient of Determination (R²), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).

Results: Of the patients, 54% were male and 43.5% were treated in surgical clinics. The mean age was 55.1 years, with 32.9% of participants aged 65 years or older. Hospital stays were 2-7 days for 39.7% of patients, over 7 days for 30.9%, and 1 day for 29.4%. Neoplasm-related diagnoses (ICD codes) accounted for 25.1% of admissions. Variables influencing LOS were identified through feature selection from patients in various hospital wards. The most significant factors affecting LOS include second diagnosis, the number of hemogram tests, computerized tomography scans (CT), ultrasonography (USG), and direct X-rays. Utilizing these factors, 12 models with varied input variables were developed and analyzed. The double hidden layer ANN model with the Levenberg-Marquardt (LM) training algorithm outperformed the others, achieving R² values of 0.854 for training and 0.807 for the test dataset, with RMSE values of 2.397 days and 2.774 days and MAE values of 1.787 days and 1.994 days, respectively. Following ANN-LM, the best results were obtained with ANFIS-GP, while MLR exhibited the lowest performance.

Conclusions: 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.