Exploring Machine Learning Algorithms for the Prediction of Dengue: A Comprehensive Review
Bibliographic record
Abstract
Vector-borne diseases, transmitted by blood-feeding arthropods like mosquitoes, ticks, and fleas, pose an escalating challenge to global public health.Dengue, a disease propagated by Aedes mosquitoes, is currently the most rapidly spreading vector-borne illness worldwide.Given its endemic nature, the prevention and control of outbreaks remain a global imperative.Timely detection of dengue is critical to mitigate mortality rates, making predictive models indispensable tools for public health planning, resource allocation, and disease control.This study undertakes a comprehensive review of various machine learning algorithms used in developing predictive models for early-stage dengue detection based on presented symptoms.The review encompasses the entire modeling process, including data preprocessing, algorithm implementation, evaluation, and validation.It further delves into the algorithms' ability to accurately classify dengue into febrile, critical, or convalescent phases.An array of machine learning algorithms, including Logistic Regression, K-Nearest Neighbor, Support Vector Machine (SVM), Decision Tree, Artificial Neural Network, and Naive Bayes Classifier were analyzed.The advantages and disadvantages of these algorithms are discussed to identify the most effective approach for dengue prediction.The Naive Bayes algorithm was found to quickly generate predictions with a precision value of 99.1%.However, the SVM model outperformed all others with a cross-validation score of 98.5%, K-Fold validation of 97.5%, precision of 98.2%, and an F1 Score of 98.0%, thereby enhancing the overall performance of the predictive model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".