Open Access Peer-reviewed Research Article

Detecting Dengue fever in children: using sequencing symptom patterns for an online assessment approach

Main Article Content

Tsair-Wei Chien
Julie Chi Chow
Yu Chang
Willy Chou corresponding author


Background: Dengue fever (DF) is an important health problem in Asia. We examined it using its clinical symptoms to predict DF.
Methods: We extracted statistically significant features from 17 DF-related clinical symptoms in 177 pediatric patients (69 diagnosed with DF) using (1) the unweighted summation score and (2) the non-parametric HT person fit statistic, which jointly combine (3) the weighted score (yielded by logistic regression) to predict DF risk.
Results: Six symptoms (Family History, Fever ≥ 39°C, Skin Rash, Petechiae, Abdominal Pain, and Weakness) significantly predicted DF. When a cutoff point of −1.03 (p = 0.26) suggested combining the weighted score and the HT coefficient, the sensitivity was 0.91 and the specificity was 0.76. The area under the ROC curve was 0.88, which was a better predictor: specificity was 5.56% higher than for the traditional logistic regression.
Conclusions: Six simple symptoms analyzed using logistic regression were useful and valid for early detection of DF risk in children. A better predictive specificity increased after combining the non-parametric HT coefficient to the weighted regression score. A self-assessment using patient smartphones is available to discriminate DF and may eliminate the need for a costly and time-consuming dengue laboratory test.

dengue fever, HT person mapping statistic, logistic regression, score summation, receiver operating characteristic curve

Article Details

How to Cite
Chien, T.-W., Chow, J. C., Chang, Y., & Chou, W. (2018). Detecting Dengue fever in children: using sequencing symptom patterns for an online assessment approach. Advances in Health and Behavior, 1(1), 12-16.


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