Detecting dengue fever in children using online Rasch analysis to develop algorithms for parents: An APP development and usability study.

ABSTRACT:

Background

Dengue fever (DF) is a significant public health concern in Asia. However, detecting the disease using traditional dichotomous criteria (i.e., absent vs present) can be extremely difficult. Convolutional neural networks (CNNs) and artificial neural networks (ANNs), due to their use of a large number of parameters for modeling, have shown the potential to improve prediction accuracy (ACC). To date, there has been no research conducted to understand item features and responses using online Rasch analysis. To verify the hypothesis that a combination of CNN, ANN, K-nearest-neighbor algorithm (KNN), and logistic regression (LR) can improve the ACC of DF prediction for children, further research is required.

Methods

We extracted 19 feature variables related to DF sympto

SUBMITTER: Hu TY 

PROVIDER: S-EPMC10063317 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature