{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T10:08:31Z","timestamp":1789380511562,"version":"build-2803163510"},"reference-count":59,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Dengue fever remains a major global health problem, and early diagnosis is challenging where confirmatory testing is limited. Machine-learning studies using routine hematological data have focused mainly on discrimination, whereas calibration, decision-analytic performance, interpretability, and robust validation have received less attention. This study aimed to develop and compare tree-based machine-learning models for dengue classification, benchmark them against L2-penalized logistic regression (LR), and evaluate discrimination, calibration, potential decision-analytic benefit, and interpretability. Methods: This retrospective secondary analysis used an open-access dataset from Bangladesh comprising 1523 patients, 18 demographic and hematological predictors, and a binary dengue test outcome. Data were divided into stratified training (80%) and test (20%) sets. The Synthetic Minority Over-sampling Technique was applied only within the training workflow. Random Forest (RF), XGBoost, and LightGBM were optimized using Optuna with stratified five-fold cross-validation. L2-penalized LR was evaluated using the same predictors and training\u2013test partition. Held-out test-set performance was assessed using AUROC, AUPRC, accuracy, sensitivity, specificity, predictive values, F1-score, and Brier score. Calibration, decision curve analysis, SHAP values, and permutation importance were also examined. Results: LightGBM, RF, and XGBoost yielded AUROCs of 0.709, 0.704, and 0.702, respectively, indicating closely similar discrimination. The primary SMOTE-trained LR yielded a numerically lower AUROC of 0.608 (95% CI: 0.536\u20130.677) and a higher Brier score of 0.310 than the tree-based models (0.174\u20130.176); however, in sensitivity analysis without SMOTE, the LR AUROC increased numerically to 0.655 and the Brier score decreased to 0.194. At the training-derived threshold of 0.558, LightGBM achieved a sensitivity of 0.914 and a specificity of 0.458, reflecting a high-sensitivity, low-specificity profile. The LightGBM calibration curve suggested closer agreement in the low-to-moderate predicted-probability range, with greater deviation at higher probabilities. Decision curve analysis suggested potential net benefit across a range of threshold probabilities but did not establish clinical utility. Platelet count, monocyte percentage, and neutrophil percentage were consistently among the leading predictors across the tree-based models. Conclusions: Tree-based models showed moderate discrimination, with high sensitivity but limited specificity, and yielded numerically higher AUROCs and lower Brier scores than the primary SMOTE-trained LR within this internal-validation framework. They should not replace etiological testing or be used as standalone diagnostic tools; their observed operating characteristics are more compatible with a potential adjunctive screening or triage-support role. External and prospective validation across independent populations and settings is required before clinical use or superiority over simpler statistical models can be established.<\/jats:p>","DOI":"10.3390\/diagnostics16182966","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T09:34:01Z","timestamp":1789378441000},"page":"2966","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Tree-Based Machine Learning for Diagnostic Classification of Dengue Fever Using Routine Hematological Parameters: A Secondary Analysis of a Publicly Available Dataset"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6950-6013","authenticated-orcid":false,"given":"Zeynep Burcin","family":"Yilmaz","sequence":"first","affiliation":[{"name":"Department of Infectious Diseases and Clinical Microbiology, Faculty of Medicine, Inonu University, Malatya 44280, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7956-9272","authenticated-orcid":false,"given":"Zeynep","family":"Kucukakcali","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya 44280, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6864-7711","authenticated-orcid":false,"given":"Sami","family":"Akbulut","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya 44280, T\u00fcrkiye"},{"name":"Department of Surgery and Liver Transplantation, Faculty of Medicine, Inonu University, Malatya 44280, T\u00fcrkiye"},{"name":"Department of Public Health, Faculty of Medicine, Inonu University, Malatya 44280, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1016\/j.molmed.2024.03.006","article-title":"Molecular mechanisms in the pathogenesis of dengue infections","volume":"30","author":"Malavige","year":"2024","journal-title":"Trends Mol. 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