In order to estimate patient length of stay (LOS) and determine the variables that affect, machine learning techniques use intricate datasets and algorithms. Support vector machines (SVMs), neural networks, decision trees, regression models, random forests, and so forth are among the most popular learning techniques. In this paper, for LOS prediction, neural networks process sequence and image data. This study uses patient data undergoing the kidney surgery at Federico II hospital based in Naples. The effectiveness of several machine learning methods was examined. Additionally, the patient characteristics that have the greatest impact on length of stay (LOS) are identified by five different types of neural networks.
Five Neural Architecture Comparison to Evaluate Performance Algorithm and Calculate Length of Stay of Patient Undergoing Kidney Surgery
Santalucia I.;
2025-01-01
Abstract
In order to estimate patient length of stay (LOS) and determine the variables that affect, machine learning techniques use intricate datasets and algorithms. Support vector machines (SVMs), neural networks, decision trees, regression models, random forests, and so forth are among the most popular learning techniques. In this paper, for LOS prediction, neural networks process sequence and image data. This study uses patient data undergoing the kidney surgery at Federico II hospital based in Naples. The effectiveness of several machine learning methods was examined. Additionally, the patient characteristics that have the greatest impact on length of stay (LOS) are identified by five different types of neural networks.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
