Predicting Predisposition to Tropical Diseases in Female Adults Using Risk Factors: An Explainable-Machine Learning Approach
Bibliographic record
Abstract
Malaria, typhoid fever, respiratory tract infections, and urinary tract infections significantly impact women, especially in remote, resource-constrained settings, due to limited access to quality healthcare and certain risk factors. Most studies have focused on vector control measures, such as insecticide-treated nets and time series analysis, often neglecting emerging yet critical risk factors vital for effectively preventing febrile diseases. We address this gap by investigating the use of machine learning (ML) models, specifically extreme gradient boost and random forest, in predicting adult females’ susceptibility to these diseases based on biological, environmental, and socioeconomic factors. An explainable AI (XAI) technique, local interpretable model-agnostic explanations (LIME), was applied to enhance the transparency and interpretability of the predictive models. This approach provided insights into the models’ decision-making process and identified key risk factors, enabling healthcare professionals to personalize treatment services. Factors such as high cholesterol levels, poor personal hygiene, and exposure to air pollution emerged as significant contributors to disease susceptibility, revealing critical areas for public health intervention in remote and resource-constrained settings. This study demonstrates the effectiveness of integrating XAI with ML in directing health interventions, providing a clearer understanding of risk factors, and efficiently allocating resources for disease prevention and treatment.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".