Dataset for the Development of AI-Powered Diagnostic Models for Vector-Borne Diseases: a perspective from Burkina Faso
Bibliographic record
Abstract
Vector-borne diseases, such as malaria, dengue, and yellow fever, pose a major public health challenge, particularly in the Hauts-Bassins region of Burkina Faso, where access to healthcare services is often limited. This dataset provides a structured collection of clinical and demographic data from 300 patients in the Dafra and Do health districts, collected over a five-week period. It is carefully annotated with patient symptoms, medical history, and laboratory-confirmed diagnoses, offering a valuable source for symptom analysis and identification of epidemiological trends. The analysis of patient records revealed that malaria was the most prevalent disease, accounting for 73.57% of cases. The majority of patients were female, representing $\mathbf{5 1. 0 2 \%}$ of the sample. Additionally, an imbalance ratio of 22.5 highlights a significant disparity in class distribution, which may lead to a biased model favoring the majority class. Furthermore, 20 symptoms were observed in at least 20% of the patients. By making this dataset available, we aim to facilitate the development of artificial intelligence models for the automatic diagnosis of vector-borne diseases. In addition, this data set serves as a valuable resource for local public health initiatives, allowing scalable disease surveillance and improving diagnostic accuracy in underserved areas.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".