DocOnTap: AI-based disease diagnostic system and recommendation system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this age of technology, Artificial Intelligence plays a key part in humankind's growth, whether in education, daily life, or professional life. AI has improved how humans live and solve problems. Using illness mapping from symptoms, the suggested method recommends relevant doctors to users. The approach attempts to boost diagnosis efficiency, reducing misdiagnoses and saving doctors' time. Machine learning algorithms and doctors' diagnoses will reduce misdiagnosis. In this work, we built a disease-based prediction system with multiple machine learning algorithms including Decision Tree, Logistic Regression and Random Forest. We obtain the highest accuracy with Random Forest classifier. After diagnosis, our system will immediately schedule an appointment for them with the most conveniently located doctor in their area, and the system's evaluation will be delivered to the appointment doctor. The proposed system is accessible through website and both doctor and patient can use then for their purposes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it