AyuPredict – A Disease Prediction Model
Bibliographic record
Abstract
The AyuPredict model is a machine learning-based healthcare system designed to predict diseases and provide Ayurvedic treatment recommendations. This system aims to bridge the gap in healthcare accessibility, particularly in remote areas where medical facilities are scarce. The model leverages unsupervised learning algorithms, such as Random Forest, for disease prediction and supervised learning algorithms, like K-Nearest Neighbours (KNN), for recommending nearby hospitals. The system's architecture includes a user-friendly interface, disease prediction module, Ayurvedic treatment recommendation module, and hospital recommendation module. The model's performance is evaluated using accuracy metrics, with the Random Forest algorithm achieving an accuracy of 99.59% and F1 score of 99.58%. The KNN algorithm is used for hospital recommendations, providing a list of nearby hospitals based on user input. Future scope includes integrating virtual consultation platforms, voice assistants, and multilingual support to enhance accessibility and usability. The AyuPredict model has the potential to revolutionize healthcare services by providing accurate disease predictions and personalized treatment recommendations, ultimately improving patient outcomes
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.007 | 0.003 |
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".