ArticleDigital health
Effectiveness of a diagnostic algorithm for dengue based on an artificial neural network.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Tree-Based Machine Learning for Diagnostic Classification of Dengue Fever Using Routine Hematological Parameters: A Secondary Analysis of a Publicly Available Dataset.Diagnostics (Basel, Switzerland) · 2026Article
- A methodological framework for conducting diagnostic meta-analyses using large literature corpora.BMC medical research methodology · 2026Article
- [Medical Practice, Epidemiology, and Evidence-Based Medicine: A necessary triangle in healthcare].Revista medica del Instituto Mexicano del Seguro Social · 2025Article
- Article
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Authors and funding
12 authors.
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Abstract
Introduction: Dengue is a disease with a wide clinical spectrum. The early identification of dengue cases is crucial but challenging for health professionals; therefore, it is necessary to have effective diagnostic instruments to initiate timely care. Objective: To evaluate the effectiveness of an algorithm based on an artificial neural network (ANN) to diagnose dengue in an endemic area. Methods: A single-center case-control study was conducted in a secondary-care hospital in Ciudad Obregón, Sonora. An algorithm was built with the official operational definitions, which was called the "direct algorithm," and for the ANN algorithm, the brain.js library was used. The data analysis was performed with the diagnostic tests of sensitivity, specificity, positive predictive value (ppv), and negative predictive value (npv), with 95% confidence intervals and Cohen's kappa index. Results: A total of 233 cases and 233 controls from 2022 were included. The ANN presented a sensitivity of 0.90 (95% CI [0.85, 0.94]), specificity of 0.82 (95% CI [0.77, 0.87]), npv of 0.91 (95% CI [0.87, 0.94]) and ppv of 0.81 (95% CI [0.76, 0.85]) and a kappa of 0.72. The direct algorithm had a sensitivity of 0.97 (95% CI [0.94, 0.99]), specificity of 0.96 (95% CI [0.92, 0.98]), npv 0.97 (95% CI [0.94, 0.98]), ppv 0.96 (95% CI [0.93, 0.98]) and kappa 0.93. Conclusions: The direct algorithm performed better than the ANN in the diagnosis of dengue.
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