Abstract
Background: Dengue fever (DF) is an important public health issue in Asia. However, the disease is extremely hard to detect using traditional dichotomous (i.e., absent vs. present) evaluations of symptoms. Convolution neural network (CNN) and artificial neural networks(ANN) can improve prediction accuracy on account of its usage of a large number of parameters for modeling. A hypothesis using a combined scheme of algorithms, including convolutional neural networks(CNN), artificial neural networks(ANN), K-nearest Neighbors Algorithm(KNN), and logis-tical regression(LR), was made to improve the prediction DF accuracy for children. Methods: We extracted 19 feature variables of DF-related symptoms from 177 pediatric patients (69 diagnosed with DF). A 11-variables were eligible by observing the statistical significance in predicting DF risk. The prediction accuracy was based on two training (80%) and testing (20%) sets on model accuracy of the area under the receiver operating characteristic curve (AUC) greater than 0.80 and 0.70, respectively, for discriminating DF+ and DF− in the two sets. Two scenarios of the combined scheme and individual algorithms were compared using the training set to predict the testing set. Results: We observed that (i) k-nearest neighbors algorithm has poorer AUC(<0.50), (ii)LR has relatively higher AUC(=0.70), and (ii) the three alternatives have almost equal AUC(=0.68), but smaller than the individual algorithms of NaiveBayes, Logistic regression in raw data and NaiveBayes in normalized data. Conclusion: An LR-based APP was designed to detect DF in children. The 11-item model is suggested to develop the APP for helping patients, family members, and clinicians discriminate DF from other febrile illnesses at an early stage.
Detecting Dengue Fever In Children Using A Combined Scheme Compared With Individual Algorithms: An APP Development And Usability Study
