Developing an Explainable Artificial Intelligence System for the Mobile-Based Diagnosis of Febrile Diseases Using Random Forest, LIME, and GPT
Bibliographic record
Abstract
OBJECTIVES: This study proposes a mobile-based explainable artificial intelligence (XAI) platform designed for diagnosing febrile illnesses. METHODS: We integrated the interpretability offered by local interpretable model-agnostic explanations (LIME) and the explainability provided by generative pre-trained transformers (GPT) to bridge the gap in understanding and trust often created by machine learning models in critical healthcare decision-making. The developed system employed random forest for disease diagnosis, LIME for interpretation of the results, and GPT-3.5 for generating explanations in easy-to-understand language. RESULTS: Our model demonstrated robust performance in detecting malaria, achieving precision, recall, and F1-scores of 85%, 91%, and 88%, respectively. It performed moderately well in detecting urinary tract and respiratory tract infections, with precision, recall, and F1-scores of 80%, 65%, and 72%, and 77%, 68%, and 72%, respectively, maintaining an effective balance between sensitivity and specificity. However, the model exhibited limitations in detecting typhoid fever and human immunodeficiency virus/acquired immune deficiency syndrome, achieving lower precision, recall, and F1-scores of 69%, 53%, and 60%, and 75%, 39%, and 51%, respectively. These results indicate missed true-positive cases, necessitating further model fine-tuning. LIME and GPT-3.5 were integrated to enhance transparency and provide natural language explanations, thereby aiding decision-making and improving user comprehension of the diagnoses. CONCLUSIONS: The LIME plots revealed key symptoms influencing the diagnoses, with bitter taste in the mouth and fever showing the highest negative influence on predictions, and GPT-3.5 provided natural language explanations that increased the reliability and trustworthiness of the system, promoting improved patient outcomes and reducing the healthcare burden.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".