A Decision Tree-Based Approach for Disease Prediction and Ayurvedic Drug Recommendation
Bibliographic record
Abstract
An Ayurvedic drug recommendation system plays a vital role in modern healthcare by offering personalized, holistic treatments based on an individual's unique constitution (Prakriti) and current health imbalances (Vikriti).By integrating ancient Ayurvedic wisdom with contemporary technology, it enhances the accessibility and accuracy of traditional treatments, ensuring they are tailored to each person's lifestyle, diet, and environment.This approach not only predicts disease but also recommend ayurvedic drug for that disease.In our work, we first forecast the sort of ailment that the patient is suffering from using the prediction interface.In the prediction interface, the patient types in his or her present symptoms.The prediction interface then predicts the illness based on the symptoms, and our decision tree model achieves 97% prediction accuracy, 97% of precision and 97% of recall.After determining the disease, ayurvedic medicine is recommended depending on age, gender, disease type, and severity.In our investigation, the recommendation model had a training accuracy of 98%, precision 93%, recall 77% and a testing accuracy of 97%, precision 88% and recall 88%.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".