LightGBM and Voting Classifier: Top Performers in Supervised Classification for Vector-Borne Diseases in Hauts-Bassins, Burkina Faso
Bibliographic record
Abstract
Vector-borne diseases remain a major public health challenge, particularly in low-income countries where access to laboratory diagnostics is limited. This study evaluates the performance of 11 supervised learning models for classifying vector-borne diseases, using a dataset of 300 patient records from Burkina Faso, where malaria accounts for approximately 79% of cases. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied. The results indicate that LightGBM and Voting Algorithm stand out as the best-performing models among those tested. Specifically, LightGBM achieved the highest accuracy on balanced datasets, with an accuracy of 98.3% and an F1-score of 98.2%. Meanwhile, the Voting Algorithm performed best on imbalanced datasets, achieving an accuracy of 86.44% and an F1-score of 83.40%. These findings highlight the importance of selecting an appropriate model based on dataset characteristics. This study emphasizes that the accuracy of vector-borne disease prediction can be significantly improved by exploring additional machine learning models. Regardless of whether the dataset is balanced or imbalanced, tailored approaches can optimize classification performance. Finally, these findings offer new perspectives on the application of artificial intelligence to enhance disease diagnosis in resource-limited settings.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".