Use of data mining techniques to classify length of stay of emergency department patients

Abstract

Abstract Emergency departments (EDs) are the largest departments of hospitals which encounter high variety of cases as well as high level of patient volumes. Thus, an efficient classification of those patients at the time of their registration is very important for the operations planning and management. Using secondary data from the ED of an urban hospital, we examine the significance of factors while classifying patients according to their length of stay. Random Forest, Classification and Regression Tree, Logistic Regression (LR), and Multilayer Perceptron (MLP) were adopted in the data set of July 2016, and these algorithms were tested in data set of August 2016. Besides adopting and testing the algorithms on the whole data set, patients in these sets were grouped into 21 based on the similarities in their diagnoses and the algorithms were also performed in these subgroups. Performances of the classifiers were evaluated based on the sensitivity, specificity, and accuracy. It was observed that sensitivity, specificity, and accuracy values of the classifiers were similar, where LR and MLP had somehow higher values. In addition, the average performance of the classifying patients within the subgroups outperformed the classifying based on the whole data set for each of the classifiers.