Identification of predictors and model for predicting prolonged length of stay in dengue patients.

ABSTRACT:

Purpose

Our objective is to identify the predictive factors and predict hospital length of stay (LOS) in dengue patients, for efficient utilization of hospital resources.

Methods

We collected 1360 medical patient records of confirmed dengue infection from 2012 to 2017 at Max group of hospitals in India. We applied two different data mining algorithms, logistic regression (LR) with elastic-net, and random forest to extract predictive factors and predict the LOS. We used an area under the curve (AUC), sensitivity, and specificity to evaluate the performance of the classifiers.

Results

The classifiers performed well, with logistic regression (LR) with elastic-net providing an AUC score of 0.75 and random forest providing a score of 0.72. Out of 1148 patients, 364 (32%) p

SUBMITTER: Shahid Ansari M 

PROVIDER: S-EPMC8363490 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature