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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".