Enhancing Public Health Outcomes: Machine Learning for Early Dengue Fever Detection in Low- and Middle-Income Countries
Bibliographic record
Abstract
Dengue fever remains a significant public health concern, especially in low- and middle-income countries (LMICs) like Nigeria, where its prevalence is driven by a combination of socioeconomic and environmental factors. This study explores the application of machine learning (ML) techniques to enhance the diagnosis of dengue fever, with a focus on rule-based classifiers to provide greater transparency and interpretability in medical decision-making. The dataset comprised over 4,800 patient records obtained from secondary and tertiary healthcare facilities in the Niger Delta region of Nigeria, with contributions from 62 experienced physicians specializing in febrile illnesses. Two rule-based classifiers, RIPPER and PART, were employed to assess their effectiveness in predicting dengue fever cases using causative data. Both models demonstrated similar performance in accuracy, sensitivity and precision indicating their strength in accurately identifying true dengue cases, which is critical for timely intervention and treatment. This study will benefit the different public stakeholders as it underscores the potential of rule-based ML models to improve the accuracy of dengue fever diagnoses in LMICs, enhancing public health efforts and optimizing resource allocation in poorer African regions where dengue is endemic.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".